Washington, D.C. Transit Accessibility Analysis
2024-12-15
EXECUTIVE SUMMARY
This study explores the relationship between socio-economic factors, particularly income levels and racial demographics, and public transportation accessibility in Washington, D.C. Given the growing emphasis on social equity in urban transportation planning, this research focuses on identifying disparities in access to public transportation, specifically addressing how these disparities affect low-income and minority communities. The findings suggest significant geographic and socio-economic patterns in transit access, revealing that poorer, predominantly minority neighborhoods face greater challenges in accessing public transportation compared to wealthier, predominantly white neighborhoods.
The study utilizes a combination of quantitative and spatial analyses to investigate the relationship between transit accessibility and socio-economic variables such as income, race, and population density. Data sources included WMATA for transit infrastructure (bus stops and Metro stations) and U.S. Census and American Community Survey (ACS) data for demographic information. The analysis incorporated a variety of statistical techniques, including regression models, cluster analysis, and geospatial mapping to identify trends and patterns.
Key Findings
- Poverty Rate: There is a positive correlation between poverty rates and transit accessibility, suggesting that neighborhoods with higher poverty rates tend to have better access to public transportation. This could be due to greater investments in transit infrastructure in these areas.
- Minority Percentage: The study finds an inverse relationship between the percentage of minority populations and transit accessibility. Higher concentrations of minority populations are associated with poorer access to transit, reflecting historical inequities in infrastructure planning.
- Population Density: Higher population density is positively correlated with better transit accessibility. Urban centers with higher population density tend to have more robust transit networks, as they are prioritized for infrastructure investments.
Cluster analysis revealed three distinct neighborhood groups based on transit accessibility and socio-economic factors: (1) neighborhoods with high transit access, low poverty, and low minority populations; (2) neighborhoods with low transit access, high poverty, and high minority populations; and (3) neighborhoods with moderate transit access and medium levels of poverty and population density. These clusters highlight the inequities faced by communities with higher poverty and minority populations.
The regression model, which explained 20% of the variability in transit access scores, demonstrated the importance of demographic factors in shaping transit accessibility. Despite its explanatory value, the model’s performance suggests that additional factors, such as proximity to employment centers or income levels, could further enhance its predictive power.
The study concludes that while Washington, D.C. has made strides in urban planning, equitable access to transit is still a challenge, especially for marginalized communities. Policymakers and transportation planners are urged to consider these findings when making decisions about transit infrastructure investments and equity-focused urban planning, particularly to improve access for low-income and minority communities. This research serves as a crucial step toward fostering a more inclusive and accessible transportation system in Washington, D.C., and can provide a model for other cities facing similar issues.
BACKGROUND
Research Question and Hypotheses
The study aims to provide answers to the following research question: - What is the relationship between income levels and racial demographics and public transportation accessibility in Washington D.C.?
Hypotheses: - H1: Lower-income neighborhoods and predominantly minority communities in Washington D.C. have less access to public transportation systems (fewer bus stops, longer distances to Metro stations) compared to wealthier, predominantly white neighborhoods. - H₀: There is no significant difference in public transportation accessibility (number of bus stops, distance to Metro stations) between lower-income, predominantly minority neighborhoods and wealthier, predominantly white neighborhoods in Washington D.C.
METHODS AND DATA
This study integrates quantitative and spatial analyses to investigate disparities in transit accessibility and its correlation with socio-economic variables. The approach combines data acquisition and cleaning, data integration, spatial processing, and analytical modeling. Together, these steps produce insights and visualizations to understand the relationship between transit access and socioeconomic factors. The methodology is designed to support informed planning decisions that address issues of accessibility and equity in transit systems.
The primary goal of this study is to visualize geographic disparities in transit access and explore the relationships between transit accessibility and socioeconomic variables such as poverty and race. This research utilizes several tools and techniques to observe its objectives. Excel was used for pre-processing and cleaning raw datasets, ensuring that the data is ready for further analysis. R was then used for spatial and statistical analysis, including mapping and regression techniques. Finally, we used Tableau for creating the final visualizations that present accessibility patterns and socioeconomic correlations, making the results easy to understand and interpret.
Data Sources
Data for the study were sourced from multiple repositories. Transit data was obtained from the Washington Metropolitan Area Transit Authority (WMATA), which provided information on bus stops, Metro stations, and service frequency. Demographic data was drawn from the U.S. Census and American Community Survey (ACS), which provided insights into income levels, poverty, race, population density, and land use types, allowing for a richer understanding of how land use patterns affect transit accessibility.
Workflow Summary
The data acquisition and cleaning process began with downloading demographic and transit data from various sources, including the U.S. Census, American Community Survey (ACS), and WMATA for bus stops and Metro stations. Raw datasets were cleaned and pre-processed using Excel, which involved removing irrelevant columns, standardizing field names (e.g., consistent GEOID), and handling missing values through imputation or removal.
Once the data was cleaned, the data integration stage took place. Datasets were merged using the GEOID field to ensure compatibility between demographic and transit data. The cleaned CSV files were then transformed into shapefiles for geospatial analysis in R, where we calculated the average distances from census tracts to the nearest Metro and bus stops. Further spatial processing was done to calculate accessibility metrics like 5-minute and 10-minute walk thresholds, which categorized areas based on their proximity to transit.
For analytical modeling, we performed correlation tests and built regression models to assess the impact of socio-economic factors, such as poverty, minority percentage, and population density, on transit accessibility. Cluster analysis was also conducted to identify patterns and disparities across neighborhoods with similar socio-economic characteristics. Finally, the geospatial data was visualized using Tableau, where interactive maps and charts were created to explore the relationship between socio-economic variables and transit access.
DATA ANALYSIS
The data analysis involved both spatial and statistical techniques to identify, quantify, and visualize disparities in transit accessibility across different neighborhoods. This process aimed to explore the correlation between transit access and socio-economic factors, such as income, race, and population density, to reveal patterns of accessibility and inequality.
Preliminary Data Insights
The initial phase of the analysis focused on cleaning the data and generating basic descriptive statistics for the demographic and transit datasets. This included calculating averages, medians, and counts for key metrics such as population density by neighborhood and proximity to transit stops. Pivot tables were used in Excel to summarize these metrics, providing an overview of the distribution of transit access and socio-economic factors across census tracts.
Geospatial Analysis
After cleaning the data, the next step was to import the data into R for spatial analysis. The cleaned CSV files were converted into shapefiles, and the average walking distance to the nearest bus and Metro stops was calculated for each census tract using spatial joins. Accessibility was then classified based on walk thresholds, including 5-minute and 10-minute walking distances. This allowed for the identification of areas with better or worse transit access across the city, providing insights into neighborhoods with more limited access to public transportation.
Temporal Analysis
A critical aspect of the analysis involved examining how transit accessibility varied by time of day, focusing on peak versus off-peak hours. Using GTFS data, we assessed service frequency during these times and mapped how accessibility changed throughout the day. The analysis revealed that peak hours generally exhibited greater accessibility, while off-peak hours showed significant disparities in access, especially in certain neighborhoods with lower socio-economic status.
Sensitivity Analysis
To assess the robustness of the analysis, a sensitivity analysis was conducted by testing various thresholds for transit access (300m, 500m, 800m distances). This was done to ensure that the findings were not overly sensitive to the assumptions about walking distance and that the results remained consistent under different parameters.
Statistical Analysis
The core statistical analysis involved performing correlation tests to explore the relationships between transit access and socio-economic variables. The results showed significant correlations between income levels, car ownership, and transit proximity. Linear regression models were developed to predict transit access disparities based on socio-economic and spatial factors, including population density and poverty levels. The models identified that neighborhoods with higher poverty rates and minority percentages generally faced greater distances to transit stations. Cluster analysis was also performed to group neighborhoods with similar transit access and socio-economic characteristics, helping to identify areas that were underserved by the current transit network.
Visualization
For effective communication of the findings, visualizations were created using Tableau. These visualizations included maps of transit access, highlighting the disparities in walking distances to Metro and bus stops. Bar charts at the ward level illustrated average transit usage and accessibility across different neighborhoods. The geospatial data was exported as GeoJSON files, which were used to create interactive maps in Tableau to allow users to explore the data dynamically.
RESULTS
Our analysis aimed to explore the relationships between transit accessibility and key demographic characteristics in Washington D.C., focusing on factors such as poverty rate, minority percentage, and population density. The model was built to investigate how these variables influence the transit access score, which was calculated as the inverse of the average distance to metro and bus stations.
The average distance to the nearest metro station in Washington, D.C., is 0.8 miles, reflecting the usual closeness of residents to transit access. The median distance is 0.7 miles, indicating that half of the population resides within 0.7 miles of a metro station, while the other half is situated at a greater distance. The first quartile (0.4 miles) indicates that 25% of the population lives in proximity to the nearest metro station, whilst the third quartile (1.1 miles) demonstrates that 75% of the population is situated within 1.1 miles of a metro station. The figures indicate that the majority of residents have convenient access to metro stations, with the interquartile range reflecting diversity in proximity based on location.
The data indicates that the mean distance to the nearest bus stop is 0.11 miles, underscoring the accessibility of bus transit alternatives for the majority of households. The median distance is 0.1 mile, signifying that half of the population resides within this distance from a bus stop, while the other half is marginally farther away. The first quartile (0.08 miles) indicates that 25% of residents reside remarkably near a bus stop, within a distance of 0.08 miles. The third quartile (0.12 miles) signifies that 75% of the population resides within 0.12 miles of a bus stop.
A spatial analysis indicates that accessibility to metro stations and bus stops is greatest in core locations, where transit infrastructure is most densely concentrated. As one approaches the periphery, accessibility diminishes, presumably due to a reduced number of transit routes extending to these regions. This pattern exemplifies the conventional structure of urban transit networks, which emphasize center areas for connectivity and demand, whilst outer districts frequently encounter diminished accessibility.
Regression Analysis
The regression model revealed significant relationships between poverty rate, minority percentage, population density, and transit accessibility.
Specifically, the analysis found that: - Poverty Rate (EP_POV150): There is a positive relationship between poverty and transit accessibility. For every 1% increase in poverty, the transit access score increased by 0.01467. This suggests that neighborhoods with higher poverty rates tend to have better access to transit options, potentially due to higher investments in public transportation in lower-income areas. The relationship is statistically significant, with a p-value of 0.00115. - Minority Percentage (EP_MINRTY): An inverse relationship was observed between minority percentage and transit accessibility. For every 1% increase in the minority percentage of a neighborhood, the transit access score decreased by 0.01409. This suggests that areas with higher minority populations may have poorer transit access, which could reflect historical inequalities in infrastructure development. The relationship is also statistically significant, with a very low p-value of 1.71e-07. - Population Density (pop_density): The regression model showed a positive relationship between population density and transit accessibility. For every increase of 1 person per square miles in population density, the transit access score increased by 0.00001132. This indicates that denser areas, which are often urban cores, tend to have better access to transit, likely due to the cluster of transit infrastructure in high-density areas. This relationship was statistically significant, with a p-value of 0.00243.
Cluster Analysis
The k-means clustering analysis, which categorized neighborhoods based on transit accessibility and demographic characteristics, revealed three distinct clusters. Cluster 1 consisted of neighborhoods with low poverty rates, low minority percentages, and high transit accessibility. Cluster 2 represented neighborhoods with high poverty rates, high minority percentages, and lower transit accessibility. Finally, Cluster 3 included areas with high population density, medium poverty levels, and moderate transit accessibility. These results suggest that neighborhoods with higher population density tend to be better served by transit, while neighborhoods with high poverty and minority populations face significant challenges in terms of access to public transportation.
Maps and Visualizations
Several visualizations were used to support the findings of this analysis. A choropleth map of Washington D.C. displayed the geographic distribution of transit accessibility scores, highlighting areas with best and worst access. Additionally, the map showed the poverty rate and minority percentages across neighborhoods, revealing areas where transit accessibility is potentially underserved, particularly in neighborhoods with higher poverty and minority percentages.
The scatter plots further confirmed the regression findings, illustrating the inverse relationship between minority percentage and transit accessibility and the positive relationship between population density and transit accessibility. These plots provided further insight into how certain demographic groups are disproportionately affected by poor transit access.
Model Performance
The overall model, with poverty rate, minority percentage, and population density as predictors, explained about 20% of the variability in transit access score (R-squared = 0.1992). Although this indicates that the model does not account for all factors influencing transit access, it does suggest that demographic characteristics play an important role.
The residual standard error was 0.7299, which indicates the average deviation from the predicted values is relatively small. However, further refinement of the model, including predictors such as income or proximity to employment centers, could improve the model’s explanatory power
CONCLUSION
This study highlights persistent inequities in public transportation accessibility across Washington, D.C., emphasizing the critical role socio-economic and demographic factors play in shaping disparities. While findings reveal neighborhoods with higher poverty rates generally have better access to bus service, areas with higher minority populations face significantly reduced accessibility.
Spatial and statistical analyses provided key insights into the relationships between transit accessibility, income, racial demographics, and population density. The positive correlation between poverty and transit access suggests targeted investments in low-income areas, while the inverse relationship with significant minority population census tracts reflects historical planning inequities that disadvantage these communities, Cluster analysis further highlighted geographic patterns of inequality, particularly in areas with high minority and poverty concentrations that remain underserved by public transportation systems.
Although Washington, D.C. has made strides in developing its transit network, the results underscore the need for more equity-focused investments and initiatives. Recommendations include prioritizing transit infrastructure investments in underserved low-income, minority neighborhoods, improving transit frequency in peripheral areas, and integrating accessibility considerations into broader urban development plans. Addressing these challenges is essential for fostering a more inclusive and equitable transit system that benefits all residents.
This research contributes to the ongoing discourse on equitable transportation planning, offering a model for identifying and addressing disparities in other metropolitan areas. Future studies could incorporate additional variables, such as employment proximity, and trip length and purpose, to deepen understanding and provide further actionable insights for policymakers.
REFERENCES
- Palmateer, C., Owen, A., Levinson, D.M., Ermagun, A., 2016. The Role of Transit Service Area Definition in Accessibility-based Evaluation. Retrieved from the University of Minnesota Digital Conservancy. http://hdl.handle.net/11299/181536
- Ermagun, A., Levinson, D., 2015. Accessibility and Transit Performance. Retrieved from the University of Minnesota Digital Conservancy. http://hdl.handle.net/11299/179832
APPENDIX: RSTUDIO WORKFLOW & OUTPUT
## To install your API key for use in future sessions, run this function with `install = TRUE`.Head: Metro & Bus Stops
## | | | 0% | |=== | 5% | |======= | 9% | |========== | 14% | |============= | 19% | |================ | 23% | |=================== | 28% | |======================= | 32% | |========================== | 37% | |============================= | 41% | |================================ | 46% | |=================================== | 50% | |======================================= | 55% | |========================================== | 60% | |============================================= | 64% | |================================================ | 69% | |=================================================== | 73% | |====================================================== | 78% | |========================================================== | 82% | |============================================================= | 87% | |================================================================ | 92% | |=================================================================== | 96% | |======================================================================| 100%## coordinates NAME
## 1 (-77.01818, 38.97609) Takoma
## 2 (-77.085, 38.95949) Friendship Heights
## 3 (-77.00221, 38.95185) Fort Totten
## 4 (-77.07959, 38.94886) Tenleytown-AU
## 5 (-77.06299, 38.94327) Van Ness-UDC
## 6 (-77.02346, 38.93744) Georgia Ave Petworth
## WEB_URL LINE
## 1 https://www.wmata.com/rider-guide/stations/takoma.cfm red
## 2 https://www.wmata.com/rider-guide/stations/friendship-hghts.cfm red
## 3 https://www.wmata.com/rider-guide/stations/fort-totten.cfm red, green
## 4 https://www.wmata.com/rider-guide/stations/tenleytown.cfm red
## 5 https://www.wmata.com/rider-guide/stations/van-ness.cfm red
## 6 https://www.wmata.com/rider-guide/stations/georgia-ave.cfm green
## ADDRESS
## 1 327 CEDAR STREET NW
## 2 5337 WISCONSIN AVENUE NW
## 3 550 GALLOWAY STREET NE
## 4 4501 WISCONSIN AVENUE NW
## 5 4200 CONNECTICUT AVENUE NW
## 6 3700 GEORGIA AVENUE NW
## TRAININFO_URL
## 1 https://www.wmata.com/js/nexttrain/nexttrain.html#B07|Takoma
## 2 https://www.wmata.com/js/nexttrain/nexttrain.html#A08|Friendship Heights
## 3 https://www.wmata.com/js/nexttrain/nexttrain.html#B06,E06|Fort Totten
## 4 https://www.wmata.com/js/nexttrain/nexttrain.html#A07|Tenleytown-AU
## 5 https://www.wmata.com/js/nexttrain/nexttrain.html#A06|Van%20Ness-UDC
## 6 https://www.wmata.com/js/nexttrain/nexttrain.html#E05|Georgia Ave-Petworth
## GIS_ID SE_ANNO_CAD_DATA OBJECTID GLOBALID
## 1 MetroStnPt_1 <NA> 1 {1F070C50-53DD-445E-951B-35A72BF2171F}
## 2 MetroStnPt_2 <NA> 2 {43309EBD-1FA3-46DC-B31A-5799591379ED}
## 3 MetroStnPt_3 <NA> 3 {696855C6-880C-440F-A222-7A98C474CBCC}
## 4 MetroStnPt_4 <NA> 4 {01C225E8-B1EA-4097-AB9E-B5C405DEE54D}
## 5 MetroStnPt_5 <NA> 5 {83724B1F-71C7-4234-832F-F750D05A1924}
## 6 MetroStnPt_6 <NA> 6 {E2DB381A-6775-4E8A-96AD-3DCF0DEBEFD8}
## CREATOR CREATED EDITOR EDITED
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## 2 <NA> <NA> <NA> <NA>
## 3 <NA> <NA> JLAY 2024-03-19 15:36:11
## 4 <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA>
## 6 <NA> <NA> JLAY 2024-03-19 15:36:11## Reading layer `LOTS' from data source
## `/Users/alexandermcroberts/Desktop/UMD/Fall 24/URSP601 - Research Methods/Final Project/LOTS.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 112399 features and 17 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -8584267 ymin: 4695877 xmax: -8561567 ymax: 4721043
## Projected CRS: WGS 84 / Pseudo-Mercator## Simple feature collection with 6 features and 17 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -8575213 ymin: 4707376 xmax: -8574385 ymax: 4709433
## Projected CRS: WGS 84 / Pseudo-Mercator
## PRIMARYLU USECODE DESCRIPTIO
## 1 Mixed 048 Commercial-Retail-Condo
## 2 Mixed 048 Commercial-Retail-Condo
## 3 Mixed 049 Commercial-Retail-Misc
## 4 Mixed 049 Commercial-Retail-Misc
## 5 Mixed 049 Commercial-Retail-Misc
## 6 Mixed 048 Commercial-Retail-Condo
## LONG_DESCR
## 1 (Class 4): Unit in a predominantly residential condo complex used for retail sales/service business.
## 2 (Class 4): Unit in a predominantly residential condo complex used for retail sales/service business.
## 3 (Class 4): All other retail commercial land uses not otherwise coded.
## 4 (Class 4): All other retail commercial land uses not otherwise coded.
## 5 (Class 4): All other retail commercial land uses not otherwise coded.
## 6 (Class 4): Unit in a predominantly residential condo complex used for retail sales/service business.
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## 6 3 0206 7000 0206 <NA> 7000 <NA> <NA> ExistingLandusePly_63
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## 4 POINT (-8574390 4707426)
## 5 POINT (-8574405 4707453)
## 6 POINT (-8575213 4709397)## Reading layer `Metro_Bus_Stops' from data source
## `/Users/alexandermcroberts/Desktop/UMD/Fall 24/URSP601 - Research Methods/Final Project/Metro_Bus_Stops.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 10044 features and 77 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -8621634 ymin: 4677973 xmax: -8534747 ymax: 4749139
## Projected CRS: WGS 84 / Pseudo-Mercator## Simple feature collection with 6 features and 77 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -8605974 ymin: 4680594 xmax: -8540493 ymax: 4715817
## Projected CRS: WGS 84 / Pseudo-Mercator
## EGIS_ID REG_ID BSTP_GEO_I BSTP_OPS_T BSTP_EFF_D BSTP_TCD AT_STR
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## ON_STR BSTP_HDG BSTP_POS_T BSTP_LDC
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## 2 MITCHELLVILLE RD 40 NEA 140' S OF ---
## 3 COLUMBIA PIKE 267 MID FRT
## 4 WAYNEWOOD BLVD 112 NEA UNKNOWN
## 5 VERNON VIEW DR 177 NEA UNKNOWN
## 6 ZION DR 297 NEA UNKNOWN
## BSTP_MSG_T BSTP_LON BSTP_LAT BSTP_LAT_L BSTP_INV_S
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## 2 MITCHELLVILLE RD + HARBOUR WAY -76.72055 38.94843 <NA> KFH
## 3 COLUMBIA PIKE + SLEEPY HOLLOW MANOR -77.17547 38.83657 <NA> KFH
## 4 WAYNEWOOD BLVD + CUSHMAN PL -77.05237 38.72474 <NA> KFH
## 5 VERNON VIEW DR + PRICES LN -77.06252 38.71264 <NA> KFH
## 6 ZION DR + SIDEBURN RD -77.30878 38.80931 <NA> KFH
## BSTP_INV_1 BSTP_BNH_C BSTP_BST_T BSTP_IFC_O BSTP_HAS_B BSTP_HAS_P BSTP_HAS_1
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## 2 2006-12-05 1 INS UNKW N N N
## 3 2012-04-20 0 UNK NAPP N N N
## 4 2012-03-26 0 UNK NAPP N N N
## 5 2012-03-26 0 UNK NAPP N N N
## 6 2012-04-20 0 UNK NAPP N N N
## BSTP_HAS_2 BSTP_PDP_S BSTP_PDP_M BSTP_PDP_O BSTP_PDP_H BSTP_HAS_3 BSTP_BDR_P
## 1 U SWK CON UNK N U U
## 2 N 58O CON SHL Y N E
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## 4 N SWK CON NON N N E
## 5 N NON EXM NON U N E
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## BSTP_SWK_W BSTP_SWK_H BSTP_SWK_1 BSTP_SWK_S BSTP_SWK_O BSTP_SWK_I ONS_CRP_SL
## 1 6 N N U UNK UNK -9.0
## 2 6 N Y N NON UNK 5.8
## 3 0 N N N NON NON 8.5
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## 5 0 N N N NON NON -9.0
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## ONS_CRP OFS_CRP AT_STR_ABS AT_STR_OSS ONS_CWK OFS_CWK AT_STR_A_1 AT_STR_O_1
## 1 Y Y U Y U U U U
## 2 Y Y Y Y Y Y Y Y
## 3 Y N N N Y N N N
## 4 N N Y E N N N E
## 5 N N N E N N N E
## 6 Y Y Y Y Y Y Y Y
## ON_STR_STS ON_STR_PDC AT_STR_PDC BSTP_PRK_R BSTP_NPK_Z CNF_NPK_SN STR_LGT_WT
## 1 U U U UNK -9 U U
## 2 Y Y Y NPL -9 U N
## 3 N N N NPL 150 N N
## 4 N N N YUN -9 N N
## 5 N N N NPL 150 N N
## 6 Y Y Y NPL 150 N N
## BSTP_HAS_L BSTP_ACC_R BSTP_BRDG_ BSTP_HAS_4 BSTP_HAS_5 METRO_ACCS WARD_ID
## 1 U N U U U U 3
## 2 U N U U U U 99
## 3 N N U U U U 99
## 4 N N U U U U 99
## 5 N N U U U U 99
## 6 N N U U U U 99
## ANC_ID SMD_ID ROUTEID MEASURE OFFSET BLOCKKEY
## 1 ANC 3E SMD 3E04 11049982 1811.9 3 05e1adbb397e3bd0d432f0c50829d68c
## 2 99 99 <NA> NA NA <NA>
## 3 99 99 <NA> NA NA <NA>
## 4 99 99 <NA> NA NA <NA>
## 5 99 99 <NA> NA NA <NA>
## 6 99 99 <NA> NA NA <NA>
## BLOCKFACEK GIS_ID SE_ANNO_CA
## 1 05e5f952d672c9d7ec508d42b1496b75 BusStopPt_31681 <NA>
## 2 <NA> BusStopPt_31682 <NA>
## 3 <NA> BusStopPt_31683 <NA>
## 4 <NA> BusStopPt_31684 <NA>
## 5 <NA> BusStopPt_31685 <NA>
## 6 <NA> BusStopPt_31686 <NA>
## GLOBALID CREATOR CREATED EDITOR EDITED
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## 4 {AE0E1A3F-7C1D-4410-9E47-01B20568BBE0} JLAY 2024-01-19 JLAY 2024-01-19
## 5 {2C8845FF-D723-4334-A3DD-4DFEA1B1C4C2} JLAY 2024-01-19 JLAY 2024-01-19
## 6 {D493E822-2BC1-4BB4-9323-24EB4E461938} JLAY 2024-01-19 JLAY 2024-01-19
## OBJECTID SNOWPRIORI BSTP_OPS_F MSTN_ID geometry
## 1 54845 <NA> NON <NA> POINT (-8581259 4715817)
## 2 54846 <NA> NON <NA> POINT (-8540493 4714288)
## 3 54847 <NA> NON <NA> POINT (-8591135 4698290)
## 4 54848 <NA> NON <NA> POINT (-8577431 4682321)
## 5 54849 <NA> NON <NA> POINT (-8578561 4680594)
## 6 54850 <NA> NON <NA> POINT (-8605974 4694395)Ensure Datasets CRS (WGS 84 (EPSG:4326)
Miles Conversion
Calculate Distances
Calculate Census Tract Average Distances
## Simple feature collection with 202 features and 2 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -8584267 ymin: 4695877 xmax: -8561567 ymax: 4721043
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 202 × 3
## GEOID avg_distance_metro_miles geometry
## <chr> <dbl> <MULTIPOINT [m]>
## 1 11001000101 0.78 ((-8577940 4708437), (-8577939 4708559)…
## 2 11001000102 1.03 ((-8578990 4709432), (-8578960 4709369)…
## 3 11001000202 1.43 ((-8579707 4709029), (-8579707 4709020)…
## 4 11001000300 1.7 ((-8580694 4710382), (-8580693 4710265)…
## 5 11001000400 0.92 ((-8579720 4711070), (-8579720 4711139)…
## 6 11001000501 0.14 ((-8577950 4710927), (-8577950 4711025)…
## 7 11001000502 0.48 ((-8578968 4711480), (-8578968 4711334)…
## 8 11001000600 0.54 ((-8579767 4712734), (-8579751 4712781)…
## 9 11001000702 1.66 ((-8580654 4710770), (-8580647 4710816)…
## 10 11001000703 1.52 ((-8580461 4711721), (-8580413 4711428)…
## # ℹ 192 more rowsSummary Statistics
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1400 0.4125 0.6700 0.8293 1.1150 2.5400## Simple feature collection with 1 feature and 7 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: -8584267 ymin: 4695877 xmax: -8561567 ymax: 4721043
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 1 × 8
## mean median sd min max q1 q3 geometry
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <MULTIPOINT [m]>
## 1 0.829 0.67 0.545 0.14 2.54 0.412 1.12 ((-8584267 4712436), (-8584261 471…Histogram: Average Distance to Metro
DC Tracts with Distances
## [1] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [10] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [19] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [28] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [37] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [46] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [55] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [64] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [73] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [82] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [91] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [100] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [109] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [118] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [127] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [136] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [145] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [154] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [163] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [172] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [181] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [190] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## [199] POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON POLYGON
## 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLEHead: Column Names and Census Tracts with Distances
## [1] TRUE## [1] "STATEFP" "COUNTYFP"
## [3] "TRACTCE" "GEOID"
## [5] "NAME" "NAMELSAD"
## [7] "MTFCC" "FUNCSTAT"
## [9] "ALAND" "AWATER"
## [11] "INTPTLAT" "INTPTLON"
## [13] "avg_distance_metro_miles" "geometry"## Simple feature collection with 6 features and 13 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -8577187 ymin: 4700689 xmax: -8568778 ymax: 4711184
## Projected CRS: WGS 84 / Pseudo-Mercator
## STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD MTFCC FUNCSTAT
## 1 11 001 004001 11001004001 40.01 Census Tract 40.01 G5020 S
## 2 11 001 004002 11001004002 40.02 Census Tract 40.02 G5020 S
## 3 11 001 003600 11001003600 36 Census Tract 36 G5020 S
## 4 11 001 004201 11001004201 42.01 Census Tract 42.01 G5020 S
## 5 11 001 004202 11001004202 42.02 Census Tract 42.02 G5020 S
## 6 11 001 007407 11001007407 74.07 Census Tract 74.07 G5020 S
## ALAND AWATER INTPTLAT INTPTLON avg_distance_metro_miles
## 1 271037 2414 +38.9208738 -077.0462674 0.57
## 2 194755 0 +38.9181186 -077.0437209 0.75
## 3 305616 0 +38.9236744 -077.0296273 0.39
## 4 204529 0 +38.9162076 -077.0388456 0.65
## 5 207646 0 +38.9134023 -077.0430254 0.38
## 6 608700 0 +38.8574823 -076.9850206 0.75
## geometry
## 1 POLYGON ((-8577187 4710396,...
## 2 POLYGON ((-8576755 4709691,...
## 3 POLYGON ((-8575208 4711159,...
## 4 POLYGON ((-8576238 4709385,...
## 5 POLYGON ((-8576721 4709432,...
## 6 POLYGON ((-8570959 4701751,...Map: Average Distances to Metro
Head: Average Bus Distance
## Simple feature collection with 202 features and 2 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -8584267 ymin: 4695877 xmax: -8561567 ymax: 4721043
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 202 × 3
## GEOID avg_distance_bus_miles geometry
## <chr> <dbl> <MULTIPOINT [m]>
## 1 11001000101 0.06 ((-8577940 4708437), (-8577939 4708559), …
## 2 11001000102 0.08 ((-8578990 4709432), (-8578960 4709369), …
## 3 11001000202 0.07 ((-8579707 4709029), (-8579707 4709020), …
## 4 11001000300 0.1 ((-8580694 4710382), (-8580693 4710265), …
## 5 11001000400 0.14 ((-8579720 4711070), (-8579720 4711139), …
## 6 11001000501 0.1 ((-8577950 4710927), (-8577950 4711025), …
## 7 11001000502 0.11 ((-8578968 4711480), (-8578968 4711334), …
## 8 11001000600 0.12 ((-8579767 4712734), (-8579751 4712781), …
## 9 11001000702 0.07 ((-8580654 4710770), (-8580647 4710816), …
## 10 11001000703 0.17 ((-8580461 4711721), (-8580413 4711428), …
## # ℹ 192 more rowsSummary Statistics
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0400 0.0800 0.1000 0.1083 0.1200 0.4000## Simple feature collection with 1 feature and 7 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: -8584267 ymin: 4695877 xmax: -8561567 ymax: 4721043
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 1 × 8
## mean median sd min max q1 q3 geometry
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <MULTIPOINT [m]>
## 1 0.108 0.1 0.0476 0.04 0.4 0.08 0.12 ((-8584267 4712436), (-8584261 47…Histogram: Average Distance to Bus Stops
Grouped Geometries
## Simple feature collection with 202 features and 2 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -8584267 ymin: 4695877 xmax: -8561567 ymax: 4721043
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 202 × 3
## GEOID avg_distance_bus_miles geometry
## * <chr> <dbl> <MULTIPOINT [m]>
## 1 11001000101 0.06 ((-8577940 4708437), (-8577939 4708559), …
## 2 11001000102 0.08 ((-8578990 4709432), (-8578960 4709369), …
## 3 11001000202 0.07 ((-8579707 4709029), (-8579707 4709020), …
## 4 11001000300 0.1 ((-8580694 4710382), (-8580693 4710265), …
## 5 11001000400 0.14 ((-8579720 4711070), (-8579720 4711139), …
## 6 11001000501 0.1 ((-8577950 4710927), (-8577950 4711025), …
## 7 11001000502 0.11 ((-8578968 4711480), (-8578968 4711334), …
## 8 11001000600 0.12 ((-8579767 4712734), (-8579751 4712781), …
## 9 11001000702 0.07 ((-8580654 4710770), (-8580647 4710816), …
## 10 11001000703 0.17 ((-8580461 4711721), (-8580413 4711428), …
## # ℹ 192 more rowsHead: Joined Census Tracts and Average Distance by Tract
## [1] TRUE## Simple feature collection with 6 features and 2 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: -8580694 ymin: 4707808 xmax: -8577168 ymax: 4711502
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 6 × 3
## GEOID avg_distance_bus_miles geometry
## <chr> <dbl> <MULTIPOINT [m]>
## 1 11001000101 0.06 ((-8577940 4708437), (-8577939 4708559), (…
## 2 11001000102 0.08 ((-8578990 4709432), (-8578960 4709369), (…
## 3 11001000202 0.07 ((-8579707 4709029), (-8579707 4709020), (…
## 4 11001000300 0.1 ((-8580694 4710382), (-8580693 4710265), (…
## 5 11001000400 0.14 ((-8579720 4711070), (-8579720 4711139), (…
## 6 11001000501 0.1 ((-8577950 4710927), (-8577950 4711025), (…Merged Summary Data (Census Tract Geometries)
## Simple feature collection with 206 features and 14 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -8584932 ymin: 4691870 xmax: -8561514 ymax: 4721076
## Projected CRS: WGS 84 / Pseudo-Mercator
## First 10 features:
## STATEFP COUNTYFP TRACTCE GEOID.x NAME NAMELSAD MTFCC FUNCSTAT
## 1 11 001 004001 11001004001 40.01 Census Tract 40.01 G5020 S
## 2 11 001 004002 11001004002 40.02 Census Tract 40.02 G5020 S
## 3 11 001 003600 11001003600 36 Census Tract 36 G5020 S
## 4 11 001 004201 11001004201 42.01 Census Tract 42.01 G5020 S
## 5 11 001 004202 11001004202 42.02 Census Tract 42.02 G5020 S
## 6 11 001 007407 11001007407 74.07 Census Tract 74.07 G5020 S
## 7 11 001 006801 11001006801 68.01 Census Tract 68.01 G5020 S
## 8 11 001 010700 11001010700 107 Census Tract 107 G5020 S
## 9 11 001 009604 11001009604 96.04 Census Tract 96.04 G5020 S
## 10 11 001 000201 11001000201 2.01 Census Tract 2.01 G5020 S
## ALAND AWATER INTPTLAT INTPTLON GEOID.y avg_distance_bus_miles
## 1 271037 2414 +38.9208738 -077.0462674 11001004001 0.10
## 2 194755 0 +38.9181186 -077.0437209 11001004002 0.09
## 3 305616 0 +38.9236744 -077.0296273 11001003600 0.10
## 4 204529 0 +38.9162076 -077.0388456 11001004201 0.10
## 5 207646 0 +38.9134023 -077.0430254 11001004202 0.17
## 6 608700 0 +38.8574823 -076.9850206 11001007407 0.08
## 7 244750 0 +38.8877413 -076.9801087 11001006801 0.07
## 8 891588 0 +38.9039988 -077.0419809 11001010700 0.06
## 9 503381 65762 +38.8931572 -076.9585043 11001009604 0.07
## 10 505004 0 +38.9092171 -077.0743418 <NA> NA
## geometry
## 1 POLYGON ((-8577187 4710396,...
## 2 POLYGON ((-8576755 4709691,...
## 3 POLYGON ((-8575208 4711159,...
## 4 POLYGON ((-8576238 4709385,...
## 5 POLYGON ((-8576721 4709432,...
## 6 POLYGON ((-8570959 4701751,...
## 7 POLYGON ((-8569781 4705520,...
## 8 POLYGON ((-8577136 4707782,...
## 9 POLYGON ((-8567511 4705977,...
## 10 POLYGON ((-8580425 4709171,...Map: Average Distance to Bus Stop
Map: Metro Stations
## Simple feature collection with 6 features and 13 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -77.085 ymin: 38.93744 xmax: -77.00221 ymax: 38.97609
## Geodetic CRS: WGS 84
## NAME
## 1 Takoma
## 2 Friendship Heights
## 3 Fort Totten
## 4 Tenleytown-AU
## 5 Van Ness-UDC
## 6 Georgia Ave Petworth
## WEB_URL LINE
## 1 https://www.wmata.com/rider-guide/stations/takoma.cfm red
## 2 https://www.wmata.com/rider-guide/stations/friendship-hghts.cfm red
## 3 https://www.wmata.com/rider-guide/stations/fort-totten.cfm red, green
## 4 https://www.wmata.com/rider-guide/stations/tenleytown.cfm red
## 5 https://www.wmata.com/rider-guide/stations/van-ness.cfm red
## 6 https://www.wmata.com/rider-guide/stations/georgia-ave.cfm green
## ADDRESS
## 1 327 CEDAR STREET NW
## 2 5337 WISCONSIN AVENUE NW
## 3 550 GALLOWAY STREET NE
## 4 4501 WISCONSIN AVENUE NW
## 5 4200 CONNECTICUT AVENUE NW
## 6 3700 GEORGIA AVENUE NW
## TRAININFO_URL
## 1 https://www.wmata.com/js/nexttrain/nexttrain.html#B07|Takoma
## 2 https://www.wmata.com/js/nexttrain/nexttrain.html#A08|Friendship Heights
## 3 https://www.wmata.com/js/nexttrain/nexttrain.html#B06,E06|Fort Totten
## 4 https://www.wmata.com/js/nexttrain/nexttrain.html#A07|Tenleytown-AU
## 5 https://www.wmata.com/js/nexttrain/nexttrain.html#A06|Van%20Ness-UDC
## 6 https://www.wmata.com/js/nexttrain/nexttrain.html#E05|Georgia Ave-Petworth
## GIS_ID SE_ANNO_CAD_DATA OBJECTID GLOBALID
## 1 MetroStnPt_1 <NA> 1 {1F070C50-53DD-445E-951B-35A72BF2171F}
## 2 MetroStnPt_2 <NA> 2 {43309EBD-1FA3-46DC-B31A-5799591379ED}
## 3 MetroStnPt_3 <NA> 3 {696855C6-880C-440F-A222-7A98C474CBCC}
## 4 MetroStnPt_4 <NA> 4 {01C225E8-B1EA-4097-AB9E-B5C405DEE54D}
## 5 MetroStnPt_5 <NA> 5 {83724B1F-71C7-4234-832F-F750D05A1924}
## 6 MetroStnPt_6 <NA> 6 {E2DB381A-6775-4E8A-96AD-3DCF0DEBEFD8}
## CREATOR CREATED EDITOR EDITED geometry
## 1 <NA> <NA> <NA> <NA> POINT (-77.01818 38.97609)
## 2 <NA> <NA> <NA> <NA> POINT (-77.085 38.95949)
## 3 <NA> <NA> JLAY 2024-03-19 15:36:11 POINT (-77.00221 38.95185)
## 4 <NA> <NA> <NA> <NA> POINT (-77.07959 38.94886)
## 5 <NA> <NA> <NA> <NA> POINT (-77.06299 38.94327)
## 6 <NA> <NA> JLAY 2024-03-19 15:36:11 POINT (-77.02346 38.93744)Merged Population and Transit Data
## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]Map: Population and Metro Stations
SVI Data
## [1] "GEOID" "avg_distance_metro_miles"
## [3] "avg_distance_bus_miles"## [1] "GEOID" "LOCATION" "AREA_SQMI" "E_TOTPOP" "E_HU"
## [6] "E_HH" "E_POV150" "E_UNEMP" "E_HBURD" "E_NOHSDP"
## [11] "E_UNINSUR" "E_AGE65" "E_AGE17" "E_DISABL" "E_SNGPNT"
## [16] "E_LIMENG" "E_MINRTY" "E_MUNIT" "E_MOBILE" "E_CROWD"
## [21] "E_NOVEH" "E_GROUPQ" "EP_POV150" "EP_UNEMP" "EP_HBURD"
## [26] "EP_NOHSDP" "EP_UNINSUR" "EP_AGE65" "EP_AGE17" "EP_DISABL"
## [31] "EP_SNGPNT" "EP_LIMENG" "EP_MINRTY" "EP_MUNIT" "EP_MOBILE"
## [36] "EP_CROWD" "EP_NOVEH" "EP_GROUPQ" "EPL_POV150" "EPL_UNEMP"
## [41] "EPL_HBURD" "EPL_NOHSDP" "EPL_UNINSUR" "SPL_THEME1" "RPL_THEME1"
## [46] "EPL_AGE65" "EPL_AGE17" "EPL_DISABL" "EPL_SNGPNT" "EPL_LIMENG"
## [51] "SPL_THEME2" "RPL_THEME2" "EPL_MINRTY" "SPL_THEME3" "RPL_THEME3"
## [56] "EPL_MUNIT" "EPL_MOBILE" "EPL_CROWD" "EPL_NOVEH" "EPL_GROUPQ"
## [61] "SPL_THEME4" "RPL_THEME4" "SPL_THEMES" "RPL_THEMES" "F_POV150"
## [66] "F_UNEMP" "F_HBURD" "F_NOHSDP" "F_UNINSUR" "F_THEME1"
## [71] "F_AGE65" "F_AGE17" "F_DISABL" "F_SNGPNT" "F_LIMENG"
## [76] "F_THEME2" "F_MINRTY" "F_THEME3" "F_MUNIT" "F_MOBILE"
## [81] "F_CROWD" "F_NOVEH" "F_GROUPQ" "F_THEME4" "F_TOTAL"- FIPS: Tract-level geographic identification
- LOCATION: Text description of tract, county, state
- AREA_SQMI: Tract area in square miles
- E_TOTPOP: Population estimate, 2018-2022 ACS
- E_HU: Housing units estimate, 2018-2022 ACS
- E_HH: Households estimate, 2018-2022 ACS
- E_POV150: Persons below 150% poverty estimate, 2018-2022 ACS
- E_UNEMP: Civilian (age 16+) unemployed estimate, 2018-2022 ACS
- E_HBURD: Housing cost-burdened occupied housing units with annual income less than $75,000 (30%+ of income spent on housing costs) estimate, 2018-2022 ACS
- E_NOHSDP: Persons (age 25+) with no high school diploma estimate, 2018-2022 ACS
- E_UNINSUR: Uninsured in the total civilian noninstitutionalized population estimate, 2018-2022 ACS
- E_AGE65: Persons aged 65 and older estimate, 2018- 2022 ACS
- E_AGE17: Persons aged 17 and younger estimate, 2018-2022 ACS
- E_DISABL: Civilian noninstitutionalized population with a disability estimate, 2018-2022 ACS
- E_SNGPNT: Single-parent household with children under 18 estimate, 2018-2022 ACS 2
- E_LIMENG: Persons (age 5+) who speak English “less than well” estimate, 2018-2022 ACS
- E_MINRTY: Minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino; Two or More Races, Not Hispanic or Latino; Other Races, Not Hispanic or Latino) estimate, 2018-2022 ACS*
- E_MUNIT: Housing in structures with 10 or more units estimate, 2018-2022 ACS
- E_MOBILE: Mobile homes estimate, 2018-2022 ACS
- E_CROWD: At household level (occupied housing units), more people than rooms estimate, 2018- 2022 ACS
- E_NOVEH: Households with no vehicle available estimate, 2018-2022 ACS
- E_GROUPQ: Persons in group quarters estimate, 2018- 2022 ACS
- EP_POV150: Percentage of persons below 150% poverty estimate
- EP_UNEMP: Unemployment Rate estimate
- EP_HBURD: Percentage of housing cost-burdened occupied housing units with annual income less than $75,000 (30%+ of income spent on housing costs) estimate, 2018-2022 ACS estimate, 2018-2022 ACS
- EP_NOHSDP: Percentage of persons with no high school diploma (age 25+) estimate
- EP_UNINSUR: Percentage uninsured in the total civilian non-institutionalized population estimate, 2018-2022 ACS
- EP_AGE65: Percentage of persons aged 65 and older estimate, 2018-2022 ACS
- EP_AGE17: Percentage of persons aged 17 and younger estimate, 2018-2022 ACS
- EP_DISABL: Percentage of civilian non-institutionalized population with a disability estimate, 2018- 2022 ACS
- EP_SNGPNT: Percentage of single-parent households with children under 18 estimate, 2018-2022 ACS
- EP_LIMENG: Percentage of persons (age 5+) who speak English “less than well” estimate, 2018-2022 ACS
- EP_MINRTY: Percentage minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino; Two or More Races, Not Hispanic or Latino; Other Races, Not Hispanic or Latino) estimate, 2018-2022 ACS
- EP_MUNIT: Percentage of housing in structures with 10 or more units estimate
- EP_MOBILE: Percentage of mobile homes estimate
- EP_CROWD: Percentage of occupied housing units with more people than rooms estimate
- EP_NOVEH: Percentage of households with no vehicle available estimate
- EP_GROUPQ: Percentage of persons in group quarters estimate, 2018-2022 ACS
- ‘geometry’: polygon coordinates for census tracts
Correlation Matrix`
## avg_distance_metro_miles avg_distance_bus_miles
## avg_distance_metro_miles 1.00000000 0.1336809
## avg_distance_bus_miles 0.13368093 1.0000000
## EP_POV150 0.05233303 -0.2534477
## EP_MINRTY 0.29128954 -0.1990139
## F_TOTAL 0.04812923 -0.1789687
## pop_density -0.33578186 -0.2416375
## EP_POV150 EP_MINRTY F_TOTAL pop_density
## avg_distance_metro_miles 0.05233303 0.2912895 0.04812923 -0.33578186
## avg_distance_bus_miles -0.25344772 -0.1990139 -0.17896869 -0.24163747
## EP_POV150 1.00000000 0.6727369 0.78355722 -0.07537901
## EP_MINRTY 0.67273694 1.0000000 0.54022066 -0.20559280
## F_TOTAL 0.78355722 0.5402207 1.00000000 -0.02743527
## pop_density -0.07537901 -0.2055928 -0.02743527 1.00000000## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## [1] "GEOID" "avg_distance_metro_miles"
## [3] "avg_distance_bus_miles" "LOCATION"
## [5] "AREA_SQMI" "E_TOTPOP"
## [7] "E_HU" "E_HH"
## [9] "E_POV150" "E_UNEMP"
## [11] "E_HBURD" "E_NOHSDP"
## [13] "E_UNINSUR" "E_AGE65"
## [15] "E_AGE17" "E_DISABL"
## [17] "E_SNGPNT" "E_LIMENG"
## [19] "E_MINRTY" "E_MUNIT"
## [21] "E_MOBILE" "E_CROWD"
## [23] "E_NOVEH" "E_GROUPQ"
## [25] "EP_POV150" "EP_UNEMP"
## [27] "EP_HBURD" "EP_NOHSDP"
## [29] "EP_UNINSUR" "EP_AGE65"
## [31] "EP_AGE17" "EP_DISABL"
## [33] "EP_SNGPNT" "EP_LIMENG"
## [35] "EP_MINRTY" "EP_MUNIT"
## [37] "EP_MOBILE" "EP_CROWD"
## [39] "EP_NOVEH" "EP_GROUPQ"
## [41] "EPL_POV150" "EPL_UNEMP"
## [43] "EPL_HBURD" "EPL_NOHSDP"
## [45] "EPL_UNINSUR" "SPL_THEME1"
## [47] "RPL_THEME1" "EPL_AGE65"
## [49] "EPL_AGE17" "EPL_DISABL"
## [51] "EPL_SNGPNT" "EPL_LIMENG"
## [53] "SPL_THEME2" "RPL_THEME2"
## [55] "EPL_MINRTY" "SPL_THEME3"
## [57] "RPL_THEME3" "EPL_MUNIT"
## [59] "EPL_MOBILE" "EPL_CROWD"
## [61] "EPL_NOVEH" "EPL_GROUPQ"
## [63] "SPL_THEME4" "RPL_THEME4"
## [65] "SPL_THEMES" "RPL_THEMES"
## [67] "F_POV150" "F_UNEMP"
## [69] "F_HBURD" "F_NOHSDP"
## [71] "F_UNINSUR" "F_THEME1"
## [73] "F_AGE65" "F_AGE17"
## [75] "F_DISABL" "F_SNGPNT"
## [77] "F_LIMENG" "F_THEME2"
## [79] "F_MINRTY" "F_THEME3"
## [81] "F_MUNIT" "F_MOBILE"
## [83] "F_CROWD" "F_NOVEH"
## [85] "F_GROUPQ" "F_THEME4"
## [87] "F_TOTAL" "NAME"
## [89] "variable" "estimate"
## [91] "moe" "geometry"
## [93] "pop_density"## 'data.frame': 201 obs. of 93 variables:
## $ GEOID : num 1.1e+10 1.1e+10 1.1e+10 1.1e+10 1.1e+10 ...
## $ avg_distance_metro_miles: num 0.78 1.03 1.43 1.7 0.92 0.14 0.48 0.54 1.66 1.52 ...
## $ avg_distance_bus_miles : num 0.06 0.08 0.07 0.1 0.14 0.1 0.11 0.12 0.07 0.17 ...
## $ LOCATION : chr "Census Tract 1.01; District of Columbia; District of Columbia" "Census Tract 1.02; District of Columbia; District of Columbia" "Census Tract 2.02; District of Columbia; District of Columbia" "Census Tract 3; District of Columbia; District of Columbia" ...
## $ AREA_SQMI : num 0.0771 0.6589 0.2998 0.4024 0.5951 ...
## $ E_TOTPOP : int 1097 3127 3919 5979 1652 3594 3384 4548 2921 2978 ...
## $ E_HU : int 841 2093 1957 2785 815 2539 1789 2290 2476 2328 ...
## $ E_HH : int 738 1866 1802 2471 660 2183 1714 2206 2168 2028 ...
## $ E_POV150 : int 47 256 476 673 169 423 218 195 297 100 ...
## $ E_UNEMP : int 0 16 107 52 29 167 49 75 31 63 ...
## $ E_HBURD : int 122 234 255 347 101 608 233 356 699 271 ...
## $ E_NOHSDP : int 12 60 35 128 37 43 43 170 0 26 ...
## $ E_UNINSUR : int 22 33 16 0 5 51 16 43 56 40 ...
## $ E_AGE65 : int 317 871 825 606 345 573 485 882 317 1558 ...
## $ E_AGE17 : int 78 301 258 1335 319 339 671 934 137 108 ...
## $ E_DISABL : int 55 211 238 135 106 236 187 364 164 547 ...
## $ E_SNGPNT : int 0 49 0 30 10 22 31 90 38 0 ...
## $ E_LIMENG : int 0 60 10 23 23 0 36 0 36 0 ...
## $ E_MINRTY : int 298 634 936 1992 546 1479 1056 966 1104 958 ...
## $ E_MUNIT : int 525 493 431 339 412 2284 1172 1181 2052 2303 ...
## $ E_MOBILE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ E_CROWD : int 4 0 9 16 0 18 47 0 65 15 ...
## $ E_NOVEH : int 293 440 523 345 89 924 359 537 723 506 ...
## $ E_GROUPQ : int 0 0 591 0 32 47 0 140 0 0 ...
## $ EP_POV150 : num 4.3 8.2 14.5 11.3 10.2 11.8 6.4 4.4 10.2 3.4 ...
## $ EP_UNEMP : num 0 0.8 4.2 1.5 3.6 6.6 2.1 2.8 1.3 4 ...
## $ EP_HBURD : num 16.5 12.5 14.2 14 15.3 27.9 13.6 16.1 32.2 13.4 ...
## $ EP_NOHSDP : num 1.2 2.2 1.4 3.2 3 1.4 1.7 4.8 0 1 ...
## $ EP_UNINSUR : num 2 1.1 0.4 0 0.3 1.4 0.5 1 1.9 1.3 ...
## $ EP_AGE65 : num 28.9 27.9 21.1 10.1 20.9 15.9 14.3 19.4 10.9 52.3 ...
## $ EP_AGE17 : num 7.1 9.6 6.6 22.3 19.3 9.4 19.8 20.5 4.7 3.6 ...
## $ EP_DISABL : num 5 6.7 6.1 2.3 6.4 6.6 5.5 8.3 5.6 18.4 ...
## $ EP_SNGPNT : num 0 2.6 0 1.2 1.5 1 1.8 4.1 1.8 0 ...
## $ EP_LIMENG : num 0 2 0.3 0.4 1.4 0 1.2 0 1.2 0 ...
## $ EP_MINRTY : num 27.2 20.3 23.9 33.3 33.1 41.2 31.2 21.2 37.8 32.2 ...
## $ EP_MUNIT : num 62.4 23.6 22 12.1 50.6 89.9 65.5 51.6 82.9 98.9 ...
## $ EP_MOBILE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ EP_CROWD : num 0.5 0 0.5 0.6 0 0.8 2.8 0 3 0.7 ...
## $ EP_NOVEH : num 39.7 23.6 29 14 13.5 42.3 20.9 24.3 33.3 25 ...
## $ EP_GROUPQ : num 0 0 15.1 0 1.9 1.3 0 3.1 0 0 ...
## $ EPL_POV150 : num 0.0683 0.2341 0.478 0.3512 0.3171 ...
## $ EPL_UNEMP : num 0 0.0537 0.4195 0.1366 0.3707 ...
## $ EPL_HBURD : num 0.3 0.158 0.232 0.217 0.261 ...
## $ EPL_NOHSDP : num 0.185 0.263 0.21 0.356 0.342 ...
## $ EPL_UNINSUR : num 0.3951 0.2195 0.0976 0 0.0878 ...
## $ SPL_THEME1 : num 0.949 0.928 1.436 1.061 1.378 ...
## $ RPL_THEME1 : num 0.1133 0.0936 0.2167 0.1281 0.197 ...
## $ EPL_AGE65 : num 0.976 0.956 0.859 0.41 0.844 ...
## $ EPL_AGE17 : num 0.176 0.234 0.161 0.654 0.585 ...
## $ EPL_DISABL : num 0.1122 0.2585 0.1902 0.0195 0.2244 ...
## $ EPL_SNGPNT : num 0 0.409 0 0.236 0.3 ...
## $ EPL_LIMENG : num 0 0.717 0.322 0.351 0.658 ...
## $ SPL_THEME2 : num 1.26 2.57 1.53 1.67 2.61 ...
## $ RPL_THEME2 : num 0.108 0.502 0.187 0.222 0.522 ...
## $ EPL_MINRTY : num 0.1024 0.0146 0.0537 0.2293 0.2195 ...
## $ SPL_THEME3 : num 0.1024 0.0146 0.0537 0.2293 0.2195 ...
## $ RPL_THEME3 : num 0.1024 0.0146 0.0537 0.2293 0.2195 ...
## $ EPL_MUNIT : num 0.69 0.271 0.256 0.158 0.591 ...
## $ EPL_MOBILE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ EPL_CROWD : num 0.192 0 0.192 0.207 0 ...
## $ EPL_NOVEH : num 0.616 0.271 0.394 0.113 0.103 ...
## $ EPL_GROUPQ : num 0 0 0.951 0 0.624 ...
## $ SPL_THEME4 : num 1.498 0.542 1.794 0.478 1.319 ...
## $ RPL_THEME4 : num 0.2365 0.0296 0.3793 0.0246 0.1823 ...
## $ SPL_THEMES : num 3.81 4.06 4.82 3.44 5.53 ...
## $ RPL_THEMES : num 0.0837 0.0936 0.1527 0.0345 0.2512 ...
## $ F_POV150 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_UNEMP : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_HBURD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_NOHSDP : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_UNINSUR : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_THEME1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_AGE65 : int 1 1 0 0 0 0 0 0 0 1 ...
## $ F_AGE17 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_DISABL : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_SNGPNT : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_LIMENG : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_THEME2 : int 1 1 0 0 0 0 0 0 0 1 ...
## $ F_MINRTY : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_THEME3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_MUNIT : int 0 0 0 0 0 1 0 0 0 1 ...
## $ F_MOBILE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_CROWD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_NOVEH : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_GROUPQ : int 0 0 1 0 0 0 0 0 0 0 ...
## $ F_THEME4 : int 0 0 1 0 0 1 0 0 0 1 ...
## $ F_TOTAL : int 1 1 1 0 0 1 0 0 0 2 ...
## $ NAME : chr "Census Tract 1.01; District of Columbia; District of Columbia" "Census Tract 1.02; District of Columbia; District of Columbia" "Census Tract 2.02; District of Columbia; District of Columbia" "Census Tract 3; District of Columbia; District of Columbia" ...
## $ variable : chr "B01003_001" "B01003_001" "B01003_001" "B01003_001" ...
## $ estimate : num 1097 3127 3919 5979 1652 ...
## $ moe : num 223 474 461 782 331 465 524 564 433 497 ...
## $ geometry :sfc_POLYGON of length 201; first list element: List of 1
## ..$ : num [1:9, 1:2] -77.1 -77.1 -77.1 -77.1 -77.1 ...
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## $ pop_density : num 0.00547 0.00142 0.00317 0.00573 0.00108 ...##
## 1 2 3
## 52 73 76Regression Model
##
## Call:
## lm(formula = avg_distance_metro_miles ~ EP_POV150 + EP_MINRTY +
## F_TOTAL + pop_density, data = combined_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.79852 -0.33037 -0.08757 0.22331 1.70218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.134e-01 1.097e-01 6.502 6.43e-10 ***
## EP_POV150 -8.456e-03 3.996e-03 -2.116 0.0356 *
## EP_MINRTY 7.543e-03 1.732e-03 4.355 2.14e-05 ***
## F_TOTAL 7.943e-03 2.935e-02 0.271 0.7870
## pop_density -1.007e-05 2.459e-06 -4.095 6.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4859 on 196 degrees of freedom
## Multiple R-squared: 0.1917, Adjusted R-squared: 0.1752
## F-statistic: 11.62 on 4 and 196 DF, p-value: 1.734e-08##
## Call:
## lm(formula = avg_distance_bus_miles ~ EP_POV150 + EP_MINRTY +
## F_TOTAL + pop_density, data = combined_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079279 -0.024022 -0.008853 0.017168 0.266395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.547e-01 1.003e-02 15.422 < 2e-16 ***
## EP_POV150 -7.323e-04 3.654e-04 -2.004 0.0464 *
## EP_MINRTY -2.420e-04 1.584e-04 -1.528 0.1281
## F_TOTAL 2.020e-03 2.683e-03 0.753 0.4526
## pop_density -9.668e-07 2.249e-07 -4.300 2.7e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04443 on 196 degrees of freedom
## Multiple R-squared: 0.1472, Adjusted R-squared: 0.1298
## F-statistic: 8.459 on 4 and 196 DF, p-value: 2.572e-06Histogram: Residuals
## Regression Analysis
Head: Transit Accessibility Scores
## GEOID.x avg_distance_metro_miles.x avg_distance_bus_miles.x
## 1 1.1001e+10 0.78 0.06
## 2 1.1001e+10 0.78 0.06
## 3 1.1001e+10 0.78 0.06
## 4 1.1001e+10 0.78 0.06
## 5 1.1001e+10 0.78 0.06
## 6 1.1001e+10 0.78 0.06
## LOCATION.x AREA_SQMI.x
## 1 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 2 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 3 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 4 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 5 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 6 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## E_TOTPOP.x E_HU.x E_HH.x E_POV150.x E_UNEMP.x E_HBURD.x E_NOHSDP.x
## 1 1097 841 738 47 0 122 12
## 2 1097 841 738 47 0 122 12
## 3 1097 841 738 47 0 122 12
## 4 1097 841 738 47 0 122 12
## 5 1097 841 738 47 0 122 12
## 6 1097 841 738 47 0 122 12
## E_UNINSUR.x E_AGE65.x E_AGE17.x E_DISABL.x E_SNGPNT.x E_LIMENG.x E_MINRTY.x
## 1 22 317 78 55 0 0 298
## 2 22 317 78 55 0 0 298
## 3 22 317 78 55 0 0 298
## 4 22 317 78 55 0 0 298
## 5 22 317 78 55 0 0 298
## 6 22 317 78 55 0 0 298
## E_MUNIT.x E_MOBILE.x E_CROWD.x E_NOVEH.x E_GROUPQ.x EP_POV150.x EP_UNEMP.x
## 1 525 0 4 293 0 -1.028569 0
## 2 525 0 4 293 0 -1.028569 0
## 3 525 0 4 293 0 -1.028569 0
## 4 525 0 4 293 0 -1.028569 0
## 5 525 0 4 293 0 -1.028569 0
## 6 525 0 4 293 0 -1.028569 0
## EP_HBURD.x EP_NOHSDP.x EP_UNINSUR.x EP_AGE65.x EP_AGE17.x EP_DISABL.x
## 1 16.5 1.2 2 28.9 7.1 5
## 2 16.5 1.2 2 28.9 7.1 5
## 3 16.5 1.2 2 28.9 7.1 5
## 4 16.5 1.2 2 28.9 7.1 5
## 5 16.5 1.2 2 28.9 7.1 5
## 6 16.5 1.2 2 28.9 7.1 5
## EP_SNGPNT.x EP_LIMENG.x EP_MINRTY.x EP_MUNIT.x EP_MOBILE.x EP_CROWD.x
## 1 0 0 -1.276997 62.4 0 0.5
## 2 0 0 -1.276997 62.4 0 0.5
## 3 0 0 -1.276997 62.4 0 0.5
## 4 0 0 -1.276997 62.4 0 0.5
## 5 0 0 -1.276997 62.4 0 0.5
## 6 0 0 -1.276997 62.4 0 0.5
## EP_NOVEH.x EP_GROUPQ.x EPL_POV150.x EPL_UNEMP.x EPL_HBURD.x EPL_NOHSDP.x
## 1 39.7 0 0.0683 0 0.3005 0.1854
## 2 39.7 0 0.0683 0 0.3005 0.1854
## 3 39.7 0 0.0683 0 0.3005 0.1854
## 4 39.7 0 0.0683 0 0.3005 0.1854
## 5 39.7 0 0.0683 0 0.3005 0.1854
## 6 39.7 0 0.0683 0 0.3005 0.1854
## EPL_UNINSUR.x SPL_THEME1.x RPL_THEME1.x EPL_AGE65.x EPL_AGE17.x EPL_DISABL.x
## 1 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 2 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 3 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 4 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 5 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 6 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## EPL_SNGPNT.x EPL_LIMENG.x SPL_THEME2.x RPL_THEME2.x EPL_MINRTY.x SPL_THEME3.x
## 1 0 0 1.2634 0.1084 0.1024 0.1024
## 2 0 0 1.2634 0.1084 0.1024 0.1024
## 3 0 0 1.2634 0.1084 0.1024 0.1024
## 4 0 0 1.2634 0.1084 0.1024 0.1024
## 5 0 0 1.2634 0.1084 0.1024 0.1024
## 6 0 0 1.2634 0.1084 0.1024 0.1024
## RPL_THEME3.x EPL_MUNIT.x EPL_MOBILE.x EPL_CROWD.x EPL_NOVEH.x EPL_GROUPQ.x
## 1 0.1024 0.6897 0 0.1921 0.6158 0
## 2 0.1024 0.6897 0 0.1921 0.6158 0
## 3 0.1024 0.6897 0 0.1921 0.6158 0
## 4 0.1024 0.6897 0 0.1921 0.6158 0
## 5 0.1024 0.6897 0 0.1921 0.6158 0
## 6 0.1024 0.6897 0 0.1921 0.6158 0
## SPL_THEME4.x RPL_THEME4.x SPL_THEMES.x RPL_THEMES.x F_POV150.x F_UNEMP.x
## 1 1.4976 0.2365 3.8127 0.0837 0 0
## 2 1.4976 0.2365 3.8127 0.0837 0 0
## 3 1.4976 0.2365 3.8127 0.0837 0 0
## 4 1.4976 0.2365 3.8127 0.0837 0 0
## 5 1.4976 0.2365 3.8127 0.0837 0 0
## 6 1.4976 0.2365 3.8127 0.0837 0 0
## F_HBURD.x F_NOHSDP.x F_UNINSUR.x F_THEME1.x F_AGE65.x F_AGE17.x F_DISABL.x
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 1 0 0
## 3 0 0 0 0 1 0 0
## 4 0 0 0 0 1 0 0
## 5 0 0 0 0 1 0 0
## 6 0 0 0 0 1 0 0
## F_SNGPNT.x F_LIMENG.x F_THEME2.x F_MINRTY.x F_THEME3.x F_MUNIT.x F_MOBILE.x
## 1 0 0 1 0 0 0 0
## 2 0 0 1 0 0 0 0
## 3 0 0 1 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 1 0 0 0 0
## 6 0 0 1 0 0 0 0
## F_CROWD.x F_NOVEH.x F_GROUPQ.x F_THEME4.x F_TOTAL.x
## 1 0 0 0 0 -0.3003133
## 2 0 0 0 0 -0.3003133
## 3 0 0 0 0 -0.3003133
## 4 0 0 0 0 -0.3003133
## 5 0 0 0 0 -0.3003133
## 6 0 0 0 0 -0.3003133
## NAME.x variable.x
## 1 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 2 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 3 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 4 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 5 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 6 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## estimate.x moe.x geometry.x pop_density.x
## 1 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 2 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 3 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 4 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 5 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 6 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## transit_access_score.x cluster.x GEOID.y avg_distance_metro_miles.y
## 1 -0.3387798 3 1.1001e+10 1.70
## 2 -0.3387798 3 1.1001e+10 0.48
## 3 -0.3387798 3 1.1001e+10 1.66
## 4 -0.3387798 3 1.1001e+10 1.28
## 5 -0.3387798 3 1.1001e+10 0.34
## 6 -0.3387798 3 1.1001e+10 1.18
## avg_distance_bus_miles.y
## 1 0.10
## 2 0.11
## 3 0.07
## 4 0.09
## 5 0.11
## 6 0.11
## LOCATION.y AREA_SQMI.y
## 1 Census Tract 3; District of Columbia; District of Columbia 0.4023785
## 2 Census Tract 5.02; District of Columbia; District of Columbia 0.2245210
## 3 Census Tract 7.02; District of Columbia; District of Columbia 0.1192928
## 4 Census Tract 8.03; District of Columbia; District of Columbia 0.1189785
## 5 Census Tract 13.04; District of Columbia; District of Columbia 0.2730340
## 6 Census Tract 18.04; District of Columbia; District of Columbia 0.2323388
## E_TOTPOP.y E_HU.y E_HH.y E_POV150.y E_UNEMP.y E_HBURD.y E_NOHSDP.y
## 1 5979 2785 2471 673 52 347 128
## 2 3384 1789 1714 218 49 233 43
## 3 2921 2476 2168 297 31 699 0
## 4 2464 1533 1233 471 86 322 0
## 5 4172 3015 2796 269 124 653 32
## 6 5166 2293 2088 1633 330 1088 520
## E_UNINSUR.y E_AGE65.y E_AGE17.y E_DISABL.y E_SNGPNT.y E_LIMENG.y E_MINRTY.y
## 1 0 606 1335 135 30 23 1992
## 2 16 485 671 187 31 36 1056
## 3 56 317 137 164 38 36 1104
## 4 226 627 416 236 0 44 938
## 5 49 658 168 405 20 0 1359
## 6 249 654 1609 736 261 754 4947
## E_MUNIT.y E_MOBILE.y E_CROWD.y E_NOVEH.y E_GROUPQ.y EP_POV150.y EP_UNEMP.y
## 1 339 0 16 345 0 -0.58428943 1.5
## 2 1172 0 47 359 0 -0.89528524 2.1
## 3 2052 0 65 723 0 -0.65410481 1.3
## 4 1105 0 0 273 103 -0.03846004 7.3
## 5 2655 0 54 710 4 -0.89528524 3.9
## 6 1243 0 228 596 34 0.70412181 11.5
## EP_HBURD.y EP_NOHSDP.y EP_UNINSUR.y EP_AGE65.y EP_AGE17.y EP_DISABL.y
## 1 14.0 3.2 0.0 10.1 22.3 2.3
## 2 13.6 1.7 0.5 14.3 19.8 5.5
## 3 32.2 0.0 1.9 10.9 4.7 5.6
## 4 26.1 0.0 9.2 25.4 16.9 9.6
## 5 23.4 0.8 1.2 15.8 4.0 9.7
## 6 52.1 15.0 4.8 12.7 31.1 14.2
## EP_SNGPNT.y EP_LIMENG.y EP_MINRTY.y EP_MUNIT.y EP_MOBILE.y EP_CROWD.y
## 1 1.2 0.4 -1.0551662 12.1 0 0.6
## 2 1.8 1.2 -1.1315342 65.5 0 2.8
## 3 1.8 1.2 -0.8915206 82.9 0 3.0
## 4 0.0 1.9 -0.8806108 72.1 0 0.0
## 5 0.7 0.0 -1.0806222 88.1 0 1.9
## 6 12.5 15.4 1.2176901 54.2 0 10.9
## EP_NOVEH.y EP_GROUPQ.y EPL_POV150.y EPL_UNEMP.y EPL_HBURD.y EPL_NOHSDP.y
## 1 14.0 0.0 0.3512 0.1366 0.2167 0.3561
## 2 20.9 0.0 0.1561 0.2244 0.2118 0.2341
## 3 33.3 0.0 0.3171 0.1073 0.7241 0.0000
## 4 22.1 4.2 0.6098 0.5951 0.5369 0.0000
## 5 25.4 0.1 0.1561 0.4000 0.4778 0.1512
## 6 28.5 0.7 0.7610 0.7610 0.9310 0.8585
## EPL_UNINSUR.y SPL_THEME1.y RPL_THEME1.y EPL_AGE65.y EPL_AGE17.y EPL_DISABL.y
## 1 0.0000 1.0606 0.1281 0.4098 0.6537 0.0195
## 2 0.1171 0.9435 0.1034 0.6732 0.5902 0.1463
## 3 0.3805 1.5290 0.2315 0.4927 0.1463 0.1512
## 4 0.9512 2.6930 0.5616 0.9415 0.4683 0.4878
## 5 0.2341 1.4192 0.2118 0.7122 0.1317 0.4976
## 6 0.7756 4.0871 0.8966 0.5854 0.9024 0.7707
## EPL_SNGPNT.y EPL_LIMENG.y SPL_THEME2.y RPL_THEME2.y EPL_MINRTY.y SPL_THEME3.y
## 1 0.2365 0.3512 1.6707 0.2217 0.2293 0.2293
## 2 0.3300 0.6195 2.3592 0.4138 0.1805 0.1805
## 3 0.3300 0.6195 1.7397 0.2512 0.2683 0.2683
## 4 0.0000 0.7024 2.6000 0.5074 0.2780 0.2780
## 5 0.1921 0.0000 1.5336 0.1921 0.2146 0.2146
## 6 0.8227 0.9902 4.0714 0.9951 0.8146 0.8146
## RPL_THEME3.y EPL_MUNIT.y EPL_MOBILE.y EPL_CROWD.y EPL_NOVEH.y EPL_GROUPQ.y
## 1 0.2293 0.1576 0 0.2069 0.1133 0.0000
## 2 0.1805 0.7044 0 0.5369 0.2167 0.0000
## 3 0.2683 0.8522 0 0.5517 0.4926 0.0000
## 4 0.2780 0.7734 0 0.0000 0.2463 0.8195
## 5 0.2146 0.8916 0 0.4581 0.3202 0.2049
## 6 0.8146 0.6305 0 0.9113 0.3842 0.4049
## SPL_THEME4.y RPL_THEME4.y SPL_THEMES.y RPL_THEMES.y F_POV150.y F_UNEMP.y
## 1 0.4778 0.0246 3.4384 0.0345 0 0
## 2 1.4580 0.2217 4.9412 0.1626 0 0
## 3 1.8965 0.4532 5.4335 0.2266 0 0
## 4 1.8392 0.3892 7.4102 0.5271 0 0
## 5 1.8748 0.4236 5.0422 0.1773 0 0
## 6 2.3309 0.6995 11.3040 0.9655 0 0
## F_HBURD.y F_NOHSDP.y F_UNINSUR.y F_THEME1.y F_AGE65.y F_AGE17.y F_DISABL.y
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 1 1 1 0 0
## 5 0 0 0 0 0 0 0
## 6 1 0 0 1 0 1 0
## F_SNGPNT.y F_LIMENG.y F_THEME2.y F_MINRTY.y F_THEME3.y F_MUNIT.y F_MOBILE.y
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 1 2 0 0 0 0
## F_CROWD.y F_NOVEH.y F_GROUPQ.y F_THEME4.y F_TOTAL.y
## 1 0 0 0 0 -0.8298129
## 2 0 0 0 0 -0.8298129
## 3 0 0 0 0 -0.8298129
## 4 0 0 0 0 0.2291864
## 5 0 0 0 0 -0.8298129
## 6 1 0 0 1 1.2881858
## NAME.y variable.y
## 1 Census Tract 3; District of Columbia; District of Columbia B01003_001
## 2 Census Tract 5.02; District of Columbia; District of Columbia B01003_001
## 3 Census Tract 7.02; District of Columbia; District of Columbia B01003_001
## 4 Census Tract 8.03; District of Columbia; District of Columbia B01003_001
## 5 Census Tract 13.04; District of Columbia; District of Columbia B01003_001
## 6 Census Tract 18.04; District of Columbia; District of Columbia B01003_001
## estimate.y moe.y geometry.y pop_density.y
## 1 5979 782 POLYGON ((-77.08257 38.9215... -0.35377270
## 2 3384 524 POLYGON ((-77.06639 38.9314... -0.33896038
## 3 2921 433 POLYGON ((-77.08257 38.9215... 0.31587132
## 4 2464 435 POLYGON ((-77.08773 38.9357... 0.05318782
## 5 4172 623 POLYGON ((-77.06165 38.9409... -0.32448736
## 6 5166 779 POLYGON ((-77.03342 38.9654... 0.15927711
## transit_access_score.y cluster.y
## 1 -1.1231108 3
## 2 0.2843645 3
## 3 -1.0953419 3
## 4 -0.9077060 3
## 5 0.9357581 3
## 6 -0.8517870 2Improved Matching
##
## The downloaded binary packages are in
## /var/folders/f7/vk15qg0n2fxcmz0j1f6v78t80000gn/T//RtmpikXqXq/downloaded_packages
Transit Accessibility and Neighborhood Comparison
Pairwise Conversion Plot
Head: Matched Pairs
## GEOID.x avg_distance_metro_miles.x avg_distance_bus_miles.x
## 1 1.1001e+10 0.78 0.06
## 2 1.1001e+10 0.78 0.06
## 3 1.1001e+10 0.78 0.06
## 4 1.1001e+10 0.78 0.06
## 5 1.1001e+10 0.78 0.06
## 6 1.1001e+10 0.78 0.06
## LOCATION.x AREA_SQMI.x
## 1 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 2 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 3 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 4 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 5 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## 6 Census Tract 1.01; District of Columbia; District of Columbia 0.07713391
## E_TOTPOP.x E_HU.x E_HH.x E_POV150.x E_UNEMP.x E_HBURD.x E_NOHSDP.x
## 1 1097 841 738 47 0 122 12
## 2 1097 841 738 47 0 122 12
## 3 1097 841 738 47 0 122 12
## 4 1097 841 738 47 0 122 12
## 5 1097 841 738 47 0 122 12
## 6 1097 841 738 47 0 122 12
## E_UNINSUR.x E_AGE65.x E_AGE17.x E_DISABL.x E_SNGPNT.x E_LIMENG.x E_MINRTY.x
## 1 22 317 78 55 0 0 298
## 2 22 317 78 55 0 0 298
## 3 22 317 78 55 0 0 298
## 4 22 317 78 55 0 0 298
## 5 22 317 78 55 0 0 298
## 6 22 317 78 55 0 0 298
## E_MUNIT.x E_MOBILE.x E_CROWD.x E_NOVEH.x E_GROUPQ.x EP_POV150.x EP_UNEMP.x
## 1 525 0 4 293 0 -1.028569 0
## 2 525 0 4 293 0 -1.028569 0
## 3 525 0 4 293 0 -1.028569 0
## 4 525 0 4 293 0 -1.028569 0
## 5 525 0 4 293 0 -1.028569 0
## 6 525 0 4 293 0 -1.028569 0
## EP_HBURD.x EP_NOHSDP.x EP_UNINSUR.x EP_AGE65.x EP_AGE17.x EP_DISABL.x
## 1 16.5 1.2 2 28.9 7.1 5
## 2 16.5 1.2 2 28.9 7.1 5
## 3 16.5 1.2 2 28.9 7.1 5
## 4 16.5 1.2 2 28.9 7.1 5
## 5 16.5 1.2 2 28.9 7.1 5
## 6 16.5 1.2 2 28.9 7.1 5
## EP_SNGPNT.x EP_LIMENG.x EP_MINRTY.x EP_MUNIT.x EP_MOBILE.x EP_CROWD.x
## 1 0 0 -1.276997 62.4 0 0.5
## 2 0 0 -1.276997 62.4 0 0.5
## 3 0 0 -1.276997 62.4 0 0.5
## 4 0 0 -1.276997 62.4 0 0.5
## 5 0 0 -1.276997 62.4 0 0.5
## 6 0 0 -1.276997 62.4 0 0.5
## EP_NOVEH.x EP_GROUPQ.x EPL_POV150.x EPL_UNEMP.x EPL_HBURD.x EPL_NOHSDP.x
## 1 39.7 0 0.0683 0 0.3005 0.1854
## 2 39.7 0 0.0683 0 0.3005 0.1854
## 3 39.7 0 0.0683 0 0.3005 0.1854
## 4 39.7 0 0.0683 0 0.3005 0.1854
## 5 39.7 0 0.0683 0 0.3005 0.1854
## 6 39.7 0 0.0683 0 0.3005 0.1854
## EPL_UNINSUR.x SPL_THEME1.x RPL_THEME1.x EPL_AGE65.x EPL_AGE17.x EPL_DISABL.x
## 1 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 2 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 3 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 4 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 5 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## 6 0.3951 0.9493 0.1133 0.9756 0.1756 0.1122
## EPL_SNGPNT.x EPL_LIMENG.x SPL_THEME2.x RPL_THEME2.x EPL_MINRTY.x SPL_THEME3.x
## 1 0 0 1.2634 0.1084 0.1024 0.1024
## 2 0 0 1.2634 0.1084 0.1024 0.1024
## 3 0 0 1.2634 0.1084 0.1024 0.1024
## 4 0 0 1.2634 0.1084 0.1024 0.1024
## 5 0 0 1.2634 0.1084 0.1024 0.1024
## 6 0 0 1.2634 0.1084 0.1024 0.1024
## RPL_THEME3.x EPL_MUNIT.x EPL_MOBILE.x EPL_CROWD.x EPL_NOVEH.x EPL_GROUPQ.x
## 1 0.1024 0.6897 0 0.1921 0.6158 0
## 2 0.1024 0.6897 0 0.1921 0.6158 0
## 3 0.1024 0.6897 0 0.1921 0.6158 0
## 4 0.1024 0.6897 0 0.1921 0.6158 0
## 5 0.1024 0.6897 0 0.1921 0.6158 0
## 6 0.1024 0.6897 0 0.1921 0.6158 0
## SPL_THEME4.x RPL_THEME4.x SPL_THEMES.x RPL_THEMES.x F_POV150.x F_UNEMP.x
## 1 1.4976 0.2365 3.8127 0.0837 0 0
## 2 1.4976 0.2365 3.8127 0.0837 0 0
## 3 1.4976 0.2365 3.8127 0.0837 0 0
## 4 1.4976 0.2365 3.8127 0.0837 0 0
## 5 1.4976 0.2365 3.8127 0.0837 0 0
## 6 1.4976 0.2365 3.8127 0.0837 0 0
## F_HBURD.x F_NOHSDP.x F_UNINSUR.x F_THEME1.x F_AGE65.x F_AGE17.x F_DISABL.x
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 1 0 0
## 3 0 0 0 0 1 0 0
## 4 0 0 0 0 1 0 0
## 5 0 0 0 0 1 0 0
## 6 0 0 0 0 1 0 0
## F_SNGPNT.x F_LIMENG.x F_THEME2.x F_MINRTY.x F_THEME3.x F_MUNIT.x F_MOBILE.x
## 1 0 0 1 0 0 0 0
## 2 0 0 1 0 0 0 0
## 3 0 0 1 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 1 0 0 0 0
## 6 0 0 1 0 0 0 0
## F_CROWD.x F_NOVEH.x F_GROUPQ.x F_THEME4.x F_TOTAL.x
## 1 0 0 0 0 -0.3003133
## 2 0 0 0 0 -0.3003133
## 3 0 0 0 0 -0.3003133
## 4 0 0 0 0 -0.3003133
## 5 0 0 0 0 -0.3003133
## 6 0 0 0 0 -0.3003133
## NAME.x variable.x
## 1 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 2 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 3 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 4 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 5 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 6 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## estimate.x moe.x geometry.x pop_density.x
## 1 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 2 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 3 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 4 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 5 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## 6 1097 223 POLYGON ((-77.05714 38.9105... -0.3980911
## transit_access_score.x cluster.x mahal_dist.x transit_access_binary.x
## 1 -0.3387798 1 2.115948 0
## 2 -0.3387798 1 2.115948 0
## 3 -0.3387798 1 2.115948 0
## 4 -0.3387798 1 2.115948 0
## 5 -0.3387798 1 2.115948 0
## 6 -0.3387798 1 2.115948 0
## GEOID.y avg_distance_metro_miles.y avg_distance_bus_miles.y
## 1 1.1001e+10 1.70 0.10
## 2 1.1001e+10 0.48 0.11
## 3 1.1001e+10 1.66 0.07
## 4 1.1001e+10 1.28 0.09
## 5 1.1001e+10 0.34 0.11
## 6 1.1001e+10 1.18 0.11
## LOCATION.y AREA_SQMI.y
## 1 Census Tract 3; District of Columbia; District of Columbia 0.4023785
## 2 Census Tract 5.02; District of Columbia; District of Columbia 0.2245210
## 3 Census Tract 7.02; District of Columbia; District of Columbia 0.1192928
## 4 Census Tract 8.03; District of Columbia; District of Columbia 0.1189785
## 5 Census Tract 13.04; District of Columbia; District of Columbia 0.2730340
## 6 Census Tract 18.04; District of Columbia; District of Columbia 0.2323388
## E_TOTPOP.y E_HU.y E_HH.y E_POV150.y E_UNEMP.y E_HBURD.y E_NOHSDP.y
## 1 5979 2785 2471 673 52 347 128
## 2 3384 1789 1714 218 49 233 43
## 3 2921 2476 2168 297 31 699 0
## 4 2464 1533 1233 471 86 322 0
## 5 4172 3015 2796 269 124 653 32
## 6 5166 2293 2088 1633 330 1088 520
## E_UNINSUR.y E_AGE65.y E_AGE17.y E_DISABL.y E_SNGPNT.y E_LIMENG.y E_MINRTY.y
## 1 0 606 1335 135 30 23 1992
## 2 16 485 671 187 31 36 1056
## 3 56 317 137 164 38 36 1104
## 4 226 627 416 236 0 44 938
## 5 49 658 168 405 20 0 1359
## 6 249 654 1609 736 261 754 4947
## E_MUNIT.y E_MOBILE.y E_CROWD.y E_NOVEH.y E_GROUPQ.y EP_POV150.y EP_UNEMP.y
## 1 339 0 16 345 0 -0.58428943 1.5
## 2 1172 0 47 359 0 -0.89528524 2.1
## 3 2052 0 65 723 0 -0.65410481 1.3
## 4 1105 0 0 273 103 -0.03846004 7.3
## 5 2655 0 54 710 4 -0.89528524 3.9
## 6 1243 0 228 596 34 0.70412181 11.5
## EP_HBURD.y EP_NOHSDP.y EP_UNINSUR.y EP_AGE65.y EP_AGE17.y EP_DISABL.y
## 1 14.0 3.2 0.0 10.1 22.3 2.3
## 2 13.6 1.7 0.5 14.3 19.8 5.5
## 3 32.2 0.0 1.9 10.9 4.7 5.6
## 4 26.1 0.0 9.2 25.4 16.9 9.6
## 5 23.4 0.8 1.2 15.8 4.0 9.7
## 6 52.1 15.0 4.8 12.7 31.1 14.2
## EP_SNGPNT.y EP_LIMENG.y EP_MINRTY.y EP_MUNIT.y EP_MOBILE.y EP_CROWD.y
## 1 1.2 0.4 -1.0551662 12.1 0 0.6
## 2 1.8 1.2 -1.1315342 65.5 0 2.8
## 3 1.8 1.2 -0.8915206 82.9 0 3.0
## 4 0.0 1.9 -0.8806108 72.1 0 0.0
## 5 0.7 0.0 -1.0806222 88.1 0 1.9
## 6 12.5 15.4 1.2176901 54.2 0 10.9
## EP_NOVEH.y EP_GROUPQ.y EPL_POV150.y EPL_UNEMP.y EPL_HBURD.y EPL_NOHSDP.y
## 1 14.0 0.0 0.3512 0.1366 0.2167 0.3561
## 2 20.9 0.0 0.1561 0.2244 0.2118 0.2341
## 3 33.3 0.0 0.3171 0.1073 0.7241 0.0000
## 4 22.1 4.2 0.6098 0.5951 0.5369 0.0000
## 5 25.4 0.1 0.1561 0.4000 0.4778 0.1512
## 6 28.5 0.7 0.7610 0.7610 0.9310 0.8585
## EPL_UNINSUR.y SPL_THEME1.y RPL_THEME1.y EPL_AGE65.y EPL_AGE17.y EPL_DISABL.y
## 1 0.0000 1.0606 0.1281 0.4098 0.6537 0.0195
## 2 0.1171 0.9435 0.1034 0.6732 0.5902 0.1463
## 3 0.3805 1.5290 0.2315 0.4927 0.1463 0.1512
## 4 0.9512 2.6930 0.5616 0.9415 0.4683 0.4878
## 5 0.2341 1.4192 0.2118 0.7122 0.1317 0.4976
## 6 0.7756 4.0871 0.8966 0.5854 0.9024 0.7707
## EPL_SNGPNT.y EPL_LIMENG.y SPL_THEME2.y RPL_THEME2.y EPL_MINRTY.y SPL_THEME3.y
## 1 0.2365 0.3512 1.6707 0.2217 0.2293 0.2293
## 2 0.3300 0.6195 2.3592 0.4138 0.1805 0.1805
## 3 0.3300 0.6195 1.7397 0.2512 0.2683 0.2683
## 4 0.0000 0.7024 2.6000 0.5074 0.2780 0.2780
## 5 0.1921 0.0000 1.5336 0.1921 0.2146 0.2146
## 6 0.8227 0.9902 4.0714 0.9951 0.8146 0.8146
## RPL_THEME3.y EPL_MUNIT.y EPL_MOBILE.y EPL_CROWD.y EPL_NOVEH.y EPL_GROUPQ.y
## 1 0.2293 0.1576 0 0.2069 0.1133 0.0000
## 2 0.1805 0.7044 0 0.5369 0.2167 0.0000
## 3 0.2683 0.8522 0 0.5517 0.4926 0.0000
## 4 0.2780 0.7734 0 0.0000 0.2463 0.8195
## 5 0.2146 0.8916 0 0.4581 0.3202 0.2049
## 6 0.8146 0.6305 0 0.9113 0.3842 0.4049
## SPL_THEME4.y RPL_THEME4.y SPL_THEMES.y RPL_THEMES.y F_POV150.y F_UNEMP.y
## 1 0.4778 0.0246 3.4384 0.0345 0 0
## 2 1.4580 0.2217 4.9412 0.1626 0 0
## 3 1.8965 0.4532 5.4335 0.2266 0 0
## 4 1.8392 0.3892 7.4102 0.5271 0 0
## 5 1.8748 0.4236 5.0422 0.1773 0 0
## 6 2.3309 0.6995 11.3040 0.9655 0 0
## F_HBURD.y F_NOHSDP.y F_UNINSUR.y F_THEME1.y F_AGE65.y F_AGE17.y F_DISABL.y
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 1 1 1 0 0
## 5 0 0 0 0 0 0 0
## 6 1 0 0 1 0 1 0
## F_SNGPNT.y F_LIMENG.y F_THEME2.y F_MINRTY.y F_THEME3.y F_MUNIT.y F_MOBILE.y
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 1 2 0 0 0 0
## F_CROWD.y F_NOVEH.y F_GROUPQ.y F_THEME4.y F_TOTAL.y
## 1 0 0 0 0 -0.8298129
## 2 0 0 0 0 -0.8298129
## 3 0 0 0 0 -0.8298129
## 4 0 0 0 0 0.2291864
## 5 0 0 0 0 -0.8298129
## 6 1 0 0 1 1.2881858
## NAME.y variable.y
## 1 Census Tract 3; District of Columbia; District of Columbia B01003_001
## 2 Census Tract 5.02; District of Columbia; District of Columbia B01003_001
## 3 Census Tract 7.02; District of Columbia; District of Columbia B01003_001
## 4 Census Tract 8.03; District of Columbia; District of Columbia B01003_001
## 5 Census Tract 13.04; District of Columbia; District of Columbia B01003_001
## 6 Census Tract 18.04; District of Columbia; District of Columbia B01003_001
## estimate.y moe.y geometry.y pop_density.y
## 1 5979 782 POLYGON ((-77.08257 38.9215... -0.35377270
## 2 3384 524 POLYGON ((-77.06639 38.9314... -0.33896038
## 3 2921 433 POLYGON ((-77.08257 38.9215... 0.31587132
## 4 2464 435 POLYGON ((-77.08773 38.9357... 0.05318782
## 5 4172 623 POLYGON ((-77.06165 38.9409... -0.32448736
## 6 5166 779 POLYGON ((-77.03342 38.9654... 0.15927711
## transit_access_score.y cluster.y mahal_dist.y transit_access_binary.y
## 1 -1.1231108 1 1.507303 0
## 2 0.2843645 1 1.639108 0
## 3 -1.0953419 1 0.820398 0
## 4 -0.9077060 1 1.378462 0
## 5 0.9357581 1 1.511806 1
## 6 -0.8517870 2 1.698502 0Sensitivity Analysis
## train_walking_category bus_walking_category n
## 1 0-5 minutes 0-5 minutes 3
## 2 0-5 minutes 6-10 minutes 1
## 3 10+ minutes 0-5 minutes 145
## 4 10+ minutes 6-10 minutes 5
## 5 6-10 minutes 0-5 minutes 45
## 6 6-10 minutes 6-10 minutes 2Head: GTFS Data (Trips)
## # A tibble: 6 × 10
## trip_id arrival_time departure_time stop_id stop_sequence stop_headsign
## <chr> <time> <time> <chr> <int> <chr>
## 1 10000120 11:10:00 11:10:00 8945 2 ""
## 2 10000120 11:12:20 11:12:20 8802 3 ""
## 3 10000120 11:12:50 11:12:50 8754 4 ""
## 4 10000120 11:13:50 11:13:50 21775 5 ""
## 5 10000120 11:15:16 11:15:16 8505 6 ""
## 6 10000120 11:16:26 11:16:26 8440 7 ""
## # ℹ 4 more variables: pickup_type <int>, drop_off_type <int>,
## # shape_dist_traveled <dbl>, timepoint <int>## # A tibble: 6 × 9
## route_id service_id trip_id trip_headsign direction_id block_id shape_id
## <chr> <chr> <chr> <chr> <int> <chr> <chr>
## 1 33 1 10000120 South to Federal … 1 W-214 33:05
## 2 L2 10 1000070 North to Chevy Ch… 0 WL-10 L2:02
## 3 D8 5 10001010 North to Washingt… 1 BD-84 D8:02
## 4 A4 6 10002110 North to Anacostia 1 SA-11 A4:01
## 5 M6 7 10003020 East to Fairfax V… 0 AM-71 M6:01
## 6 M6 8 10003060 East to Fairfax V… 0 AM-71 M6:01
## # ℹ 2 more variables: scheduled_trip_id <chr>, train_id <chr>Head: Peak Vs Off-Peak Hours
## # A tibble: 6 × 11
## trip_id arrival_time departure_time stop_id stop_sequence stop_headsign
## <chr> <time> <dttm> <chr> <int> <chr>
## 1 10000120 11:10:00 1970-01-01 11:10:00 8945 2 ""
## 2 10000120 11:12:20 1970-01-01 11:12:20 8802 3 ""
## 3 10000120 11:12:50 1970-01-01 11:12:50 8754 4 ""
## 4 10000120 11:13:50 1970-01-01 11:13:50 21775 5 ""
## 5 10000120 11:15:16 1970-01-01 11:15:16 8505 6 ""
## 6 10000120 11:16:26 1970-01-01 11:16:26 8440 7 ""
## # ℹ 5 more variables: pickup_type <int>, drop_off_type <int>,
## # shape_dist_traveled <dbl>, timepoint <int>, time_period <chr>Head: Transit Frequency by Time Period
## # A tibble: 6 × 3
## # Groups: stop_id [6]
## stop_id time_period trip_count
## <chr> <chr> <int>
## 1 100 Non-Peak 28
## 2 1000 Non-Peak 707
## 3 10000 Non-Peak 700
## 4 10001 Non-Peak 236
## 5 10002 Non-Peak 290
## 6 10003 Non-Peak 300Head: Non-Standard Work Hours
## Simple feature collection with 6 features and 5 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -77.07902 ymin: 38.87284 xmax: -76.92403 ymax: 38.9843
## Geodetic CRS: NAD83
## GEOID NAME
## 1 11001000201 Census Tract 2.01; District of Columbia; District of Columbia
## 2 11001010300 Census Tract 103; District of Columbia; District of Columbia
## 3 11001002801 Census Tract 28.01; District of Columbia; District of Columbia
## 4 11001004002 Census Tract 40.02; District of Columbia; District of Columbia
## 5 11001006700 Census Tract 67; District of Columbia; District of Columbia
## 6 11001007707 Census Tract 77.07; District of Columbia; District of Columbia
## variable estimate moe geometry
## 1 B23025_004 791 147 POLYGON ((-77.07902 38.9126...
## 2 B23025_004 2361 418 POLYGON ((-77.03636 38.9748...
## 3 B23025_004 2344 351 POLYGON ((-77.03645 38.9349...
## 4 B23025_004 2425 473 POLYGON ((-77.04627 38.9166...
## 5 B23025_004 2457 323 POLYGON ((-76.99496 38.8898...
## 6 B23025_004 1622 440 POLYGON ((-76.94486 38.8790...Head: SF Object, Stops with Geo, and Merged Stops with Geo and ACS by GEOID
## Simple feature collection with 6 features and 17 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -76.98712 ymin: 38.71979 xmax: -76.90452 ymax: 38.99686
## Geodetic CRS: WGS 84
## # A tibble: 6 × 18
## stop_id stop_code stop_name stop_desc zone_id stop_url location_type
## <chr> <chr> <chr> <chr> <chr> <chr> <int>
## 1 100 3000098 Old Fort Rd+Old Pi… "" "" "" NA
## 2 1000 3000380 Birchwood Dr+Fount… "" "" "" NA
## 3 10000 3002273 New Hampshire Av+Q… "" "" "" NA
## 4 10001 3002274 Riggs Rd+Ruatan St "" "" "" NA
## 5 10002 3002275 Greenbelt Rd+63 Av "" "" "" NA
## 6 10003 3002276 Greenbelt Rd+63 Av "" "" "" NA
## # ℹ 11 more variables: parent_station <chr>, stop_timezone <chr>,
## # wheelchair_boarding <int>, level_id <chr>, platform_code <chr>,
## # geometry <POINT [°]>, GEOID <chr>, NAME <chr>, variable <chr>,
## # estimate <dbl>, moe <dbl>## GEOID stop_id time_period trip_count stop_code
## 1 11001000101 20420 Non-Peak 260 1001463
## 2 11001000101 6464 Non-Peak 256 1001435
## 3 11001000101 6442 Non-Peak 256 1001418
## 4 11001000101 6544 Non-Peak 707 1001487
## 5 11001000101 6434 Non-Peak 256 1001412
## 6 11001000101 6448 Non-Peak 260 1001424
## stop_name stop_desc zone_id stop_url location_type
## 1 P St NW+Rock Creek Pkwy NW NA
## 2 P St NW+Rock Creek Pkwy NW NA
## 3 P St NW+26 St NW NA
## 4 Q St NW+27 St NW NA
## 5 28 St NW+P St NW NA
## 6 P St NW+27 St NW NA
## parent_station stop_timezone wheelchair_boarding level_id platform_code
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## geometry.x
## 1 POINT (-77.05224 38.90973)
## 2 POINT (-77.052 38.9096)
## 3 POINT (-77.05488 38.90931)
## 4 POINT (-77.05518 38.91051)
## 5 POINT (-77.05701 38.90926)
## 6 POINT (-77.05541 38.90944)
## NAME.x variable.x
## 1 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 2 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 3 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 4 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 5 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## 6 Census Tract 1.01; District of Columbia; District of Columbia B01003_001
## estimate.x moe.x
## 1 1097 265.7021
## 2 1097 265.7021
## 3 1097 265.7021
## 4 1097 265.7021
## 5 1097 265.7021
## 6 1097 265.7021
## NAME.y variable.y
## 1 Census Tract 1.01; District of Columbia; District of Columbia B23025_004
## 2 Census Tract 1.01; District of Columbia; District of Columbia B23025_004
## 3 Census Tract 1.01; District of Columbia; District of Columbia B23025_004
## 4 Census Tract 1.01; District of Columbia; District of Columbia B23025_004
## 5 Census Tract 1.01; District of Columbia; District of Columbia B23025_004
## 6 Census Tract 1.01; District of Columbia; District of Columbia B23025_004
## estimate.y moe.y geometry.y
## 1 688 193 POLYGON ((-77.05714 38.9105...
## 2 688 193 POLYGON ((-77.05714 38.9105...
## 3 688 193 POLYGON ((-77.05714 38.9105...
## 4 688 193 POLYGON ((-77.05714 38.9105...
## 5 688 193 POLYGON ((-77.05714 38.9105...
## 6 688 193 POLYGON ((-77.05714 38.9105...Head: Non Standard Hours Accessibility
## # A tibble: 6 × 3
## GEOID avg_trip_count high_non_standard_hours
## <chr> <dbl> <dbl>
## 1 11001000101 358. 688
## 2 11001000102 802. 2079
## 3 11001000201 433 791
## 4 11001000202 581. 2435
## 5 11001000300 428. 3447
## 6 11001000400 542. 784Bar Plot: Transit Accessibility by Neighborhood (Non-Standard Work Hours)
## Correlation Matrix
Cluster Analysis
## [1] "GEOID" "avg_distance_metro_miles"
## [3] "avg_distance_bus_miles" "LOCATION"
## [5] "AREA_SQMI" "E_TOTPOP"
## [7] "E_HU" "E_HH"
## [9] "E_POV150" "E_UNEMP"
## [11] "E_HBURD" "E_NOHSDP"
## [13] "E_UNINSUR" "E_AGE65"
## [15] "E_AGE17" "E_DISABL"
## [17] "E_SNGPNT" "E_LIMENG"
## [19] "E_MINRTY" "E_MUNIT"
## [21] "E_MOBILE" "E_CROWD"
## [23] "E_NOVEH" "E_GROUPQ"
## [25] "EP_POV150" "EP_UNEMP"
## [27] "EP_HBURD" "EP_NOHSDP"
## [29] "EP_UNINSUR" "EP_AGE65"
## [31] "EP_AGE17" "EP_DISABL"
## [33] "EP_SNGPNT" "EP_LIMENG"
## [35] "EP_MINRTY" "EP_MUNIT"
## [37] "EP_MOBILE" "EP_CROWD"
## [39] "EP_NOVEH" "EP_GROUPQ"
## [41] "EPL_POV150" "EPL_UNEMP"
## [43] "EPL_HBURD" "EPL_NOHSDP"
## [45] "EPL_UNINSUR" "SPL_THEME1"
## [47] "RPL_THEME1" "EPL_AGE65"
## [49] "EPL_AGE17" "EPL_DISABL"
## [51] "EPL_SNGPNT" "EPL_LIMENG"
## [53] "SPL_THEME2" "RPL_THEME2"
## [55] "EPL_MINRTY" "SPL_THEME3"
## [57] "RPL_THEME3" "EPL_MUNIT"
## [59] "EPL_MOBILE" "EPL_CROWD"
## [61] "EPL_NOVEH" "EPL_GROUPQ"
## [63] "SPL_THEME4" "RPL_THEME4"
## [65] "SPL_THEMES" "RPL_THEMES"
## [67] "F_POV150" "F_UNEMP"
## [69] "F_HBURD" "F_NOHSDP"
## [71] "F_UNINSUR" "F_THEME1"
## [73] "F_AGE65" "F_AGE17"
## [75] "F_DISABL" "F_SNGPNT"
## [77] "F_LIMENG" "F_THEME2"
## [79] "F_MINRTY" "F_THEME3"
## [81] "F_MUNIT" "F_MOBILE"
## [83] "F_CROWD" "F_NOVEH"
## [85] "F_GROUPQ" "F_THEME4"
## [87] "F_TOTAL" "train_walking_category"
## [89] "bus_walking_category"## 'data.frame': 201 obs. of 89 variables:
## $ GEOID : num 1.1e+10 1.1e+10 1.1e+10 1.1e+10 1.1e+10 ...
## $ avg_distance_metro_miles: num 0.78 1.03 1.43 1.7 0.92 0.14 0.48 0.54 1.66 1.52 ...
## $ avg_distance_bus_miles : num 0.06 0.08 0.07 0.1 0.14 0.1 0.11 0.12 0.07 0.17 ...
## $ LOCATION : chr "Census Tract 1.01; District of Columbia; District of Columbia" "Census Tract 1.02; District of Columbia; District of Columbia" "Census Tract 2.02; District of Columbia; District of Columbia" "Census Tract 3; District of Columbia; District of Columbia" ...
## $ AREA_SQMI : num 0.0771 0.6589 0.2998 0.4024 0.5951 ...
## $ E_TOTPOP : int 1097 3127 3919 5979 1652 3594 3384 4548 2921 2978 ...
## $ E_HU : int 841 2093 1957 2785 815 2539 1789 2290 2476 2328 ...
## $ E_HH : int 738 1866 1802 2471 660 2183 1714 2206 2168 2028 ...
## $ E_POV150 : int 47 256 476 673 169 423 218 195 297 100 ...
## $ E_UNEMP : int 0 16 107 52 29 167 49 75 31 63 ...
## $ E_HBURD : int 122 234 255 347 101 608 233 356 699 271 ...
## $ E_NOHSDP : int 12 60 35 128 37 43 43 170 0 26 ...
## $ E_UNINSUR : int 22 33 16 0 5 51 16 43 56 40 ...
## $ E_AGE65 : int 317 871 825 606 345 573 485 882 317 1558 ...
## $ E_AGE17 : int 78 301 258 1335 319 339 671 934 137 108 ...
## $ E_DISABL : int 55 211 238 135 106 236 187 364 164 547 ...
## $ E_SNGPNT : int 0 49 0 30 10 22 31 90 38 0 ...
## $ E_LIMENG : int 0 60 10 23 23 0 36 0 36 0 ...
## $ E_MINRTY : int 298 634 936 1992 546 1479 1056 966 1104 958 ...
## $ E_MUNIT : int 525 493 431 339 412 2284 1172 1181 2052 2303 ...
## $ E_MOBILE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ E_CROWD : int 4 0 9 16 0 18 47 0 65 15 ...
## $ E_NOVEH : int 293 440 523 345 89 924 359 537 723 506 ...
## $ E_GROUPQ : int 0 0 591 0 32 47 0 140 0 0 ...
## $ EP_POV150 : num 4.3 8.2 14.5 11.3 10.2 11.8 6.4 4.4 10.2 3.4 ...
## $ EP_UNEMP : num 0 0.8 4.2 1.5 3.6 6.6 2.1 2.8 1.3 4 ...
## $ EP_HBURD : num 16.5 12.5 14.2 14 15.3 27.9 13.6 16.1 32.2 13.4 ...
## $ EP_NOHSDP : num 1.2 2.2 1.4 3.2 3 1.4 1.7 4.8 0 1 ...
## $ EP_UNINSUR : num 2 1.1 0.4 0 0.3 1.4 0.5 1 1.9 1.3 ...
## $ EP_AGE65 : num 28.9 27.9 21.1 10.1 20.9 15.9 14.3 19.4 10.9 52.3 ...
## $ EP_AGE17 : num 7.1 9.6 6.6 22.3 19.3 9.4 19.8 20.5 4.7 3.6 ...
## $ EP_DISABL : num 5 6.7 6.1 2.3 6.4 6.6 5.5 8.3 5.6 18.4 ...
## $ EP_SNGPNT : num 0 2.6 0 1.2 1.5 1 1.8 4.1 1.8 0 ...
## $ EP_LIMENG : num 0 2 0.3 0.4 1.4 0 1.2 0 1.2 0 ...
## $ EP_MINRTY : num 27.2 20.3 23.9 33.3 33.1 41.2 31.2 21.2 37.8 32.2 ...
## $ EP_MUNIT : num 62.4 23.6 22 12.1 50.6 89.9 65.5 51.6 82.9 98.9 ...
## $ EP_MOBILE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ EP_CROWD : num 0.5 0 0.5 0.6 0 0.8 2.8 0 3 0.7 ...
## $ EP_NOVEH : num 39.7 23.6 29 14 13.5 42.3 20.9 24.3 33.3 25 ...
## $ EP_GROUPQ : num 0 0 15.1 0 1.9 1.3 0 3.1 0 0 ...
## $ EPL_POV150 : num 0.0683 0.2341 0.478 0.3512 0.3171 ...
## $ EPL_UNEMP : num 0 0.0537 0.4195 0.1366 0.3707 ...
## $ EPL_HBURD : num 0.3 0.158 0.232 0.217 0.261 ...
## $ EPL_NOHSDP : num 0.185 0.263 0.21 0.356 0.342 ...
## $ EPL_UNINSUR : num 0.3951 0.2195 0.0976 0 0.0878 ...
## $ SPL_THEME1 : num 0.949 0.928 1.436 1.061 1.378 ...
## $ RPL_THEME1 : num 0.1133 0.0936 0.2167 0.1281 0.197 ...
## $ EPL_AGE65 : num 0.976 0.956 0.859 0.41 0.844 ...
## $ EPL_AGE17 : num 0.176 0.234 0.161 0.654 0.585 ...
## $ EPL_DISABL : num 0.1122 0.2585 0.1902 0.0195 0.2244 ...
## $ EPL_SNGPNT : num 0 0.409 0 0.236 0.3 ...
## $ EPL_LIMENG : num 0 0.717 0.322 0.351 0.658 ...
## $ SPL_THEME2 : num 1.26 2.57 1.53 1.67 2.61 ...
## $ RPL_THEME2 : num 0.108 0.502 0.187 0.222 0.522 ...
## $ EPL_MINRTY : num 0.1024 0.0146 0.0537 0.2293 0.2195 ...
## $ SPL_THEME3 : num 0.1024 0.0146 0.0537 0.2293 0.2195 ...
## $ RPL_THEME3 : num 0.1024 0.0146 0.0537 0.2293 0.2195 ...
## $ EPL_MUNIT : num 0.69 0.271 0.256 0.158 0.591 ...
## $ EPL_MOBILE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ EPL_CROWD : num 0.192 0 0.192 0.207 0 ...
## $ EPL_NOVEH : num 0.616 0.271 0.394 0.113 0.103 ...
## $ EPL_GROUPQ : num 0 0 0.951 0 0.624 ...
## $ SPL_THEME4 : num 1.498 0.542 1.794 0.478 1.319 ...
## $ RPL_THEME4 : num 0.2365 0.0296 0.3793 0.0246 0.1823 ...
## $ SPL_THEMES : num 3.81 4.06 4.82 3.44 5.53 ...
## $ RPL_THEMES : num 0.0837 0.0936 0.1527 0.0345 0.2512 ...
## $ F_POV150 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_UNEMP : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_HBURD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_NOHSDP : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_UNINSUR : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_THEME1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_AGE65 : int 1 1 0 0 0 0 0 0 0 1 ...
## $ F_AGE17 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_DISABL : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_SNGPNT : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_LIMENG : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_THEME2 : int 1 1 0 0 0 0 0 0 0 1 ...
## $ F_MINRTY : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_THEME3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_MUNIT : int 0 0 0 0 0 1 0 0 0 1 ...
## $ F_MOBILE : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_CROWD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_NOVEH : int 0 0 0 0 0 0 0 0 0 0 ...
## $ F_GROUPQ : int 0 0 1 0 0 0 0 0 0 0 ...
## $ F_THEME4 : int 0 0 1 0 0 1 0 0 0 1 ...
## $ F_TOTAL : int 1 1 1 0 0 1 0 0 0 2 ...
## $ train_walking_category : chr "10+ minutes" "10+ minutes" "10+ minutes" "10+ minutes" ...
## $ bus_walking_category : chr "0-5 minutes" "0-5 minutes" "0-5 minutes" "0-5 minutes" ...##
## 1 2 3
## 52 73 76Regression Analysis 2
##
## Call:
## lm(formula = transit_access_score ~ EP_POV150 + EP_MINRTY + pop_density,
## data = combined_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3330 -0.4553 -0.1635 0.4130 2.6458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.816e+00 1.630e-01 11.146 < 2e-16 ***
## EP_POV150 1.467e-02 4.448e-03 3.299 0.00115 **
## EP_MINRTY -1.409e-02 2.599e-03 -5.423 1.71e-07 ***
## pop_density 1.132e-05 3.687e-06 3.071 0.00243 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7299 on 197 degrees of freedom
## Multiple R-squared: 0.1992, Adjusted R-squared: 0.187
## F-statistic: 16.34 on 3 and 197 DF, p-value: 1.6e-09VIF Multicollinearity
## EP_POV150 EP_MINRTY pop_density
## 1.844286 1.917750 1.054791Histogram of Residuals
Summary of Interaction Model
##
## Call:
## lm(formula = transit_access_score ~ EP_POV150 * EP_MINRTY + pop_density,
## data = combined_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3051 -0.4425 -0.1480 0.2734 2.6016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.314e+00 2.226e-01 5.905 1.53e-08 ***
## EP_POV150 5.710e-02 1.385e-02 4.122 5.54e-05 ***
## EP_MINRTY -6.511e-03 3.461e-03 -1.881 0.06140 .
## pop_density 9.631e-06 3.640e-06 2.646 0.00881 **
## EP_POV150:EP_MINRTY -5.147e-04 1.595e-04 -3.226 0.00147 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7131 on 196 degrees of freedom
## Multiple R-squared: 0.2396, Adjusted R-squared: 0.2241
## F-statistic: 15.44 on 4 and 196 DF, p-value: 5.379e-11